[GitHub] ankkhedia commented on a change in pull request #12664: [MXNET-637] Multidimensional LSTM example for MXNetR

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ankkhedia commented on a change in pull request #12664: [MXNET-637] Multidimensional LSTM example for MXNetR
URL: https://github.com/apache/incubator-mxnet/pull/12664#discussion_r220386508
 
 

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 File path: R-package/vignettes/MultidimLstm.Rmd
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+LSTM time series example
+=============================================
+
+This tutorial shows how to use an LSTM model with multivariate data, and generate predictions from it. For demonstration purposes, we used an opensource pollution data. You can find the data on [GitHub](https://github.com/dmlc/web-data/tree/master/mxnet/tinyshakespeare).
+The tutorial is an illustration of how to use LSTM models with MXNetR. We are forecasting the air pollution with data recorded at the US embassy in Beijing, China for five years.
+
+Dataset Attribution:
+"PM2.5 data of US Embassy in Beijing" (https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)
+We want to predict pollution levels(PM2.5 concentration) in the city given the above dataset.
+
+```r
+Dataset description:
+No: row number
+year: year of data in this row
+month: month of data in this row
+day: day of data in this row
+hour: hour of data in this row
+pm2.5: PM2.5 concentration
+DEWP: Dew Point
+TEMP: Temperature
+PRES: Pressure
+cbwd: Combined wind direction
+Iws: Cumulated wind speed
+Is: Cumulated hours of snow
+Ir: Cumulated hours of rain
+```
+
+We use past PM2.5 concentration, dew point, temperature, pressure, wind speed, snow and rain to predict
+PM2.5 concentration levels
+
+Load  and pre-process the Data
+---------
+Load in the data and preprocess it. It is assumed that the data has been downloaded in as csv file 'data.csv' locally.
+
+ ```r
+    ## Loading required packages
+    library("readr")
+    library("dplyr")
+    library("mxnet")
+    library("abind")
+ ```
+
+
+
+ ```r
+    ## Preprocessing steps
+
+    Data <- read.csv(file="data.csv", header=TRUE, sep=",")
+
+    ## Extracting specific features from the dataset as variables for time series
+    ## We extract pollution, temperature, pressue, windspeed, snowfall and rainfall information from dataset
+
+    df<-data.frame(Data$pm2.5, Data$DEWP,Data$TEMP, Data$PRES, Data$Iws, Data$Is, Data$Ir)
+    df[is.na(df)] <- 0
+
+    ## Now we normalise each of the feature set to a range(0,1)
+    df<-matrix(as.matrix(df),ncol=ncol(df),dimnames=NULL)
+    rangenorm <- function(x){(x-min(x))/(max(x)-min(x))}
+    df <- apply(df,2, rangenorm)
+    df<-t(df)
+  ```
+For using multidimesional data with MXNetR. We need to convert training data to the form
+(n_dim x seq_len x num_samples) and label should be of the form (seq_len x num_samples) or (1 x num_samples)
+depending on the LSTM flavour to be used(one-to-one/ many-to-one). Please note that MXNetR currently supports only these two flavours of RNN.
+We have used n_dim =7, seq_len = 100  and num_samples= 430.
 
 Review comment:
   cool will do that

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