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Posted to commits@iotdb.apache.org by cr...@apache.org on 2026/04/16 03:02:53 UTC
(iotdb-docs) branch main updated: remove invalid udf function (#1069)
This is an automated email from the ASF dual-hosted git repository.
critas pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/iotdb-docs.git
The following commit(s) were added to refs/heads/main by this push:
new e3d7de2e remove invalid udf function (#1069)
e3d7de2e is described below
commit e3d7de2e94ed92c9375da184cc669a92dba670ea
Author: leto-b <bi...@timecho.com>
AuthorDate: Thu Apr 16 11:02:47 2026 +0800
remove invalid udf function (#1069)
---
.../Master/Tree/SQL-Manual/UDF-Libraries_apache.md | 563 --------------------
.../Tree/SQL-Manual/UDF-Libraries_timecho.md | 562 --------------------
.../V1.3.x/SQL-Manual/UDF-Libraries_apache.md | 562 --------------------
.../V1.3.x/SQL-Manual/UDF-Libraries_timecho.md | 561 --------------------
.../dev-1.3/SQL-Manual/UDF-Libraries_apache.md | 562 --------------------
.../dev-1.3/SQL-Manual/UDF-Libraries_timecho.md | 563 --------------------
.../latest/SQL-Manual/UDF-Libraries_apache.md | 563 --------------------
.../latest/SQL-Manual/UDF-Libraries_timecho.md | 562 --------------------
.../Master/Tree/SQL-Manual/UDF-Libraries_apache.md | 565 --------------------
.../Tree/SQL-Manual/UDF-Libraries_timecho.md | 565 --------------------
.../V1.3.x/SQL-Manual/UDF-Libraries_apache.md | 565 --------------------
.../V1.3.x/SQL-Manual/UDF-Libraries_timecho.md | 565 --------------------
.../dev-1.3/SQL-Manual/UDF-Libraries_apache.md | 585 ---------------------
.../dev-1.3/SQL-Manual/UDF-Libraries_timecho.md | 585 ---------------------
.../latest/SQL-Manual/UDF-Libraries_apache.md | 563 --------------------
.../latest/SQL-Manual/UDF-Libraries_timecho.md | 565 --------------------
16 files changed, 9056 deletions(-)
diff --git a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
index b06feda5..92ab43de 100644
--- a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
@@ -3052,259 +3052,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 5. Frequency Domain Analysis
### 5.1 Conv
@@ -4823,192 +4570,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 8. Series Discovery
### 8.1 ConsecutiveSequences
@@ -5262,127 +4823,3 @@ Output Series:
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
index 8da9225d..d7d18873 100644
--- a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
@@ -3052,259 +3052,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 5. Frequency Domain Analysis
### 5.1 Conv
@@ -4882,192 +4629,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 8. Series Discovery
### 8.1 ConsecutiveSequences
@@ -5322,126 +4883,3 @@ Output Series:
+-----------------------------+---------------------------+
```
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
index 3f5261bd..d0939f86 100644
--- a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
@@ -3055,259 +3055,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Frequency Domain Analysis
### Conv
@@ -4824,192 +4571,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Series Discovery
### ConsecutiveSequences
@@ -5264,126 +4825,3 @@ Output Series:
+-----------------------------+---------------------------+
```
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
index d655754c..a9f33d65 100644
--- a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
@@ -3054,259 +3054,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Frequency Domain Analysis
### Conv
@@ -4883,192 +4630,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Series Discovery
### ConsecutiveSequences
@@ -5323,126 +4884,4 @@ Output Series:
+-----------------------------+---------------------------+
```
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
diff --git a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
index 3f5261bd..d0939f86 100644
--- a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
@@ -3055,259 +3055,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Frequency Domain Analysis
### Conv
@@ -4824,192 +4571,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Series Discovery
### ConsecutiveSequences
@@ -5264,126 +4825,3 @@ Output Series:
+-----------------------------+---------------------------+
```
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
index d655754c..679fdcf3 100644
--- a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
@@ -3054,259 +3054,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Frequency Domain Analysis
### Conv
@@ -4883,192 +4630,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## Series Discovery
### ConsecutiveSequences
@@ -5322,127 +4883,3 @@ Output Series:
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
index b06feda5..92ab43de 100644
--- a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
@@ -3052,259 +3052,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 5. Frequency Domain Analysis
### 5.1 Conv
@@ -4823,192 +4570,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 8. Series Discovery
### 8.1 ConsecutiveSequences
@@ -5262,127 +4823,3 @@ Output Series:
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
index 8da9225d..d7d18873 100644
--- a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
@@ -3052,259 +3052,6 @@ Output series:
+-----------------------------+--------------------------------------------------------+
```
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:** MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:** MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| true|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 5. Frequency Domain Analysis
### 5.1 Conv
@@ -4882,192 +4629,6 @@ Output series:
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used.
-
-Input series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 8. Series Discovery
### 8.1 ConsecutiveSequences
@@ -5322,126 +4883,3 @@ Output Series:
+-----------------------------+---------------------------+
```
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:**
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series:
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
index 323272dc..2f7b2804 100644
--- a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
@@ -3076,261 +3076,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `p`:模型阶数。
-- `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `p`:模型阶数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-- `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-- `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-- `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 5. 频域分析
### 5.1 Conv
@@ -4883,192 +4628,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-\*\*函数名：\*\*MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-\*\*输出序列：\*\*输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 8. 序列发现
### 8.1 ConsecutiveSequences
@@ -5320,127 +4879,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb ` ,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb ` ,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
index a8e6f961..27512790 100644
--- a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
@@ -3109,262 +3109,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 5. 频域分析
### 5.1 Conv
@@ -4916,191 +4660,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 8. 序列发现
### 8.1 ConsecutiveSequences
@@ -5354,127 +4913,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
index ea230b60..cf35a323 100644
--- a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
@@ -3109,262 +3109,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 频域分析
### Conv
@@ -4929,191 +4673,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 序列发现
### ConsecutiveSequences
@@ -5367,127 +4926,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
index 865959d4..418cd935 100644
--- a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
@@ -3109,262 +3109,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 频域分析
### Conv
@@ -4915,191 +4659,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 序列发现
### ConsecutiveSequences
@@ -5353,127 +4912,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
index ea230b60..dc86b7bb 100644
--- a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
@@ -3109,262 +3109,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 频域分析
### Conv
@@ -4929,191 +4673,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 序列发现
### ConsecutiveSequences
@@ -5278,26 +4837,6 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1
+-----------------------------+--------------------------------------------------------------------+
```
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
## 机器学习
@@ -5367,127 +4906,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
index 865959d4..4821c3a7 100644
--- a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
@@ -3109,262 +3109,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 频域分析
### Conv
@@ -4915,190 +4659,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
## 序列发现
@@ -5264,27 +4824,6 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1
+-----------------------------+--------------------------------------------------------------------+
```
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 机器学习
### AR
@@ -5353,127 +4892,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
index 323272dc..52d1ee87 100644
--- a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
@@ -3076,260 +3076,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `p`:模型阶数。
-- `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `p`:模型阶数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-- `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-- `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-- `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
## 5. 频域分析
@@ -4883,191 +4629,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-\*\*函数名：\*\*MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-\*\*输出序列：\*\*输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
## 8. 序列发现
@@ -5320,127 +4881,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb ` ,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb ` ,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
index a8e6f961..27512790 100644
--- a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
@@ -3109,262 +3109,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
+-----------------------------+--------------------------------------------------------+
```
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null|
-|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null|
-|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null|
-|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null|
-|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 116.327274|
-|1970-01-01T08:00:00.002+08:00| 116.327305|
-|1970-01-01T08:00:00.003+08:00| 116.3273291|
-|1970-01-01T08:00:00.004+08:00| 116.327342|
-|1970-01-01T08:00:00.005+08:00| 116.3273744|
-|1970-01-01T08:00:00.006+08:00| 116.3274117|
-|1970-01-01T08:00:00.007+08:00| 116.3274396|
-|1970-01-01T08:00:00.008+08:00| 116.3274668|
-|1970-01-01T08:00:00.009+08:00| 116.3275026|
-|1970-01-01T08:00:00.010+08:00| 116.3274967|
-|1970-01-01T08:00:00.011+08:00| 116.3274929|
-|1970-01-01T08:00:00.012+08:00| 116.3274745|
-|1970-01-01T08:00:00.013+08:00| 116.3275095|
-|1970-01-01T08:00:00.014+08:00| 116.3274787|
-|1970-01-01T08:00:00.015+08:00| 116.3274693|
-|1970-01-01T08:00:00.016+08:00| 116.3274941|
-|1970-01-01T08:00:00.017+08:00| 116.3275401|
-|1970-01-01T08:00:00.018+08:00| 116.3275713|
-|1970-01-01T08:00:00.019+08:00| 116.3276003|
-|1970-01-01T08:00:00.020+08:00| 116.3276308|
-|1970-01-01T08:00:00.021+08:00| 116.3276338|
-|1970-01-01T08:00:00.022+08:00| 116.3276684|
-|1970-01-01T08:00:00.023+08:00| 116.3277016|
-|1970-01-01T08:00:00.024+08:00| 116.3277284|
-|1970-01-01T08:00:00.025+08:00| 116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| false|
-|1970-01-01T08:00:00.002+08:00| false|
-|1970-01-01T08:00:00.003+08:00| false|
-|1970-01-01T08:00:00.004+08:00| false|
-|1970-01-01T08:00:00.005+08:00| true|
-|1970-01-01T08:00:00.006+08:00| false|
-|1970-01-01T08:00:00.007+08:00| false|
-|1970-01-01T08:00:00.008+08:00| false|
-|1970-01-01T08:00:00.009+08:00| false|
-|1970-01-01T08:00:00.010+08:00| false|
-|1970-01-01T08:00:00.011+08:00| false|
-|1970-01-01T08:00:00.012+08:00| false|
-|1970-01-01T08:00:00.013+08:00| false|
-|1970-01-01T08:00:00.014+08:00| true|
-|1970-01-01T08:00:00.015+08:00| false|
-|1970-01-01T08:00:00.016+08:00| false|
-|1970-01-01T08:00:00.017+08:00| false|
-|1970-01-01T08:00:00.018+08:00| false|
-|1970-01-01T08:00:00.019+08:00| false|
-|1970-01-01T08:00:00.020+08:00| false|
-|1970-01-01T08:00:00.021+08:00| false|
-|1970-01-01T08:00:00.022+08:00| false|
-|1970-01-01T08:00:00.023+08:00| false|
-|1970-01-01T08:00:00.024+08:00| false|
-|1970-01-01T08:00:00.025+08:00| false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 5. 频域分析
### 5.1 Conv
@@ -4916,191 +4660,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
+-----------------------------+-------------------------------------------------+
```
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00| 1704|
-|2021-07-01T12:00:02.000+08:00| 1702|
-|2021-07-01T12:00:03.000+08:00| 1702|
-|2021-07-01T12:00:04.000+08:00| 1701|
-|2021-07-01T12:00:07.000+08:00| 1703|
-|2021-07-01T12:00:08.000+08:00| 1704|
-|2021-07-01T12:01:09.000+08:00| 1705|
-|2021-07-01T12:01:10.000+08:00| 1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 101.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 87.0|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00| 100.0|
-|2020-01-01T00:00:04.000+08:00| 120.0|
-|2020-01-01T00:00:06.000+08:00| 80.0|
-|2020-01-01T00:00:08.000+08:00| 100.5|
-|2020-01-01T00:00:10.000+08:00| 119.5|
-|2020-01-01T00:00:12.000+08:00| 81.5|
-|2020-01-01T00:00:14.000+08:00| 99.5|
-|2020-01-01T00:00:16.000+08:00| 119.0|
-|2020-01-01T00:00:18.000+08:00| 80.5|
-|2020-01-01T00:00:20.000+08:00| 99.0|
-|2020-01-01T00:00:22.000+08:00| 121.0|
-|2020-01-01T00:00:24.000+08:00| 79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
- Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
--->
-
## 8. 序列发现
### 8.1 ConsecutiveSequences
@@ -5354,127 +4913,3 @@ select ar(s0,"p"="2") from root.test.d0
|1970-01-01T08:00:00.002+08:00| -0.2571|
+-----------------------------+---------------------------+
```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:** Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-| Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0|
-|2020-01-01T00:00:03.000+08:00| -2.0|
-|2020-01-01T00:00:04.000+08:00| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1|
-|1970-01-01T08:00:00.002+08:00| 1|
-|1970-01-01T08:00:00.003+08:00| 0|
-|1970-01-01T08:00:00.004+08:00| 0|
-|1970-01-01T08:00:00.005+08:00| 1|
-|1970-01-01T08:00:00.006+08:00| 1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:** RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:**
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-| Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00| -4.0| -4.0|
-|2020-01-01T00:00:02.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:03.000+08:00| -3.0| -3.0|
-|2020-01-01T00:00:04.000+08:00| -1.0| -1.0|
-|2020-01-01T00:00:05.000+08:00| 0.0| 0.0|
-|2020-01-01T00:00:06.000+08:00| 1.0| 1.0|
-|2020-01-01T00:00:07.000+08:00| 2.0| 2.0|
-|2020-01-01T00:00:08.000+08:00| 3.0| 3.0|
-|2020-01-01T00:00:09.000+08:00| 4.0| 4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00| 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-