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Posted to commits@pinot.apache.org by xh...@apache.org on 2019/04/16 23:41:24 UTC
[incubator-pinot] branch master updated: [TE] Holt Winters detector
(#4067)
This is an automated email from the ASF dual-hosted git repository.
xhsun pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-pinot.git
The following commit(s) were added to refs/heads/master by this push:
new 6cd8657 [TE] Holt Winters detector (#4067)
6cd8657 is described below
commit 6cd8657b939eb0c0832e2353b47deac1a7d3632d
Author: Dian Tang <de...@gmail.com>
AuthorDate: Tue Apr 16 16:41:18 2019 -0700
[TE] Holt Winters detector (#4067)
---
.../datasource/sql/SqlThirdEyeDataSource.java | 1 -
.../detection/components/HoltWintersDetector.java | 570 ++++
.../detection/spec/HoltWintersDetectorSpec.java | 89 +
.../yaml/CompositePipelineConfigTranslator.java | 3 +-
.../components/HoltWintersDetectorTest.java | 159 ++
.../PercentageChangeRuleDetectorTest.java | 1 -
.../pinot/thirdeye/detection/algorithm/daily.csv | 732 +++++
.../pinot/thirdeye/detection/algorithm/hourly.csv | 3000 ++++++++++++++++++++
8 files changed, 4552 insertions(+), 3 deletions(-)
diff --git a/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/datasource/sql/SqlThirdEyeDataSource.java b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/datasource/sql/SqlThirdEyeDataSource.java
index eb72276..1c5d114 100644
--- a/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/datasource/sql/SqlThirdEyeDataSource.java
+++ b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/datasource/sql/SqlThirdEyeDataSource.java
@@ -89,7 +89,6 @@ public class SqlThirdEyeDataSource implements ThirdEyeDataSource {
} catch (Exception e) {
throw e;
}
-
}
/**
diff --git a/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetector.java b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetector.java
new file mode 100644
index 0000000..36efb29
--- /dev/null
+++ b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetector.java
@@ -0,0 +1,570 @@
+/*
+ * 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.
+ */
+package org.apache.pinot.thirdeye.detection.components;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.concurrent.TimeUnit;
+import org.apache.commons.math3.analysis.MultivariateFunction;
+import org.apache.commons.math3.optim.PointValuePair;
+import org.apache.pinot.thirdeye.dataframe.BooleanSeries;
+import org.apache.pinot.thirdeye.dataframe.DataFrame;
+import org.apache.pinot.thirdeye.dataframe.DoubleSeries;
+import org.apache.pinot.thirdeye.dataframe.LongSeries;
+import org.apache.pinot.thirdeye.dataframe.Series;
+import org.apache.pinot.thirdeye.dataframe.util.MetricSlice;
+import org.apache.pinot.thirdeye.datalayer.dto.DatasetConfigDTO;
+import org.apache.pinot.thirdeye.datalayer.dto.MergedAnomalyResultDTO;
+import org.apache.pinot.thirdeye.detection.ConfigUtils;
+import org.apache.pinot.thirdeye.detection.DetectionUtils;
+import org.apache.pinot.thirdeye.detection.InputDataFetcher;
+import org.apache.pinot.thirdeye.detection.Pattern;
+import org.apache.pinot.thirdeye.detection.algorithm.AlgorithmUtils;
+import org.apache.pinot.thirdeye.detection.annotation.Components;
+import org.apache.pinot.thirdeye.detection.annotation.DetectionTag;
+import org.apache.pinot.thirdeye.detection.annotation.Param;
+import org.apache.pinot.thirdeye.detection.spec.HoltWintersDetectorSpec;
+import org.apache.pinot.thirdeye.detection.spi.components.AnomalyDetector;
+import org.apache.pinot.thirdeye.detection.spi.components.BaselineProvider;
+import org.apache.pinot.thirdeye.detection.spi.model.InputData;
+import org.apache.pinot.thirdeye.detection.spi.model.InputDataSpec;
+import org.apache.pinot.thirdeye.detection.spi.model.TimeSeries;
+import org.apache.pinot.thirdeye.rootcause.impl.MetricEntity;
+import org.joda.time.DateTime;
+import org.joda.time.DateTimeZone;
+import org.joda.time.Interval;
+import org.joda.time.Period;
+import org.apache.commons.math3.optim.MaxIter;
+import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction;
+import org.apache.commons.math3.optim.MaxEval;
+import org.apache.commons.math3.optim.SimpleBounds;
+import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer;
+import org.apache.commons.math3.optim.InitialGuess;
+import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import static org.apache.pinot.thirdeye.dataframe.util.DataFrameUtils.*;
+
+/**
+ * Holt-Winters forecasting algorithm with multiplicative method
+ * Supports seasonality and trend detection
+ * https://otexts.com/fpp2/holt-winters.html
+ */
+@Components(title = "Holt Winters triple exponential smoothing forecasting and detection",
+ type = "HOLT_WINTERS_RULE",
+ tags = {DetectionTag.RULE_DETECTION},
+ description = "Forecast with holt winters triple exponential smoothing and generate anomalies",
+ params = {
+ @Param(name = "alpha"),
+ @Param(name = "beta"),
+ @Param(name = "gamma"),
+ @Param(name = "period"),
+ @Param(name = "pattern"),
+ @Param(name = "sensitivity"),
+ @Param(name = "kernelSmoothing")})
+public class HoltWintersDetector implements BaselineProvider<HoltWintersDetectorSpec>,
+ AnomalyDetector<HoltWintersDetectorSpec> {
+ private static final Logger LOG = LoggerFactory.getLogger(HoltWintersDetector.class);
+ private InputDataFetcher dataFetcher;
+ private static final String COL_CURR = "current";
+ private static final String COL_BASE = "baseline";
+ private static final String COL_ANOMALY = "anomaly";
+ private static final String COL_PATTERN = "pattern";
+ private static final String COL_DIFF = "diff";
+ private static final String COL_DIFF_VIOLATION = "diff_violation";
+ private static final String COL_ERROR = "error";
+ private static final long KERNEL_PERIOD = 3600000L;
+ private static final int LOOKBACK = 60;
+
+ private int period;
+ private double alpha;
+ private double beta;
+ private double gamma;
+ private Pattern pattern;
+ private double sensitivity;
+ private boolean smoothing;
+ private Period lookbackPeriod = ConfigUtils.parsePeriod(LOOKBACK + "DAYS");
+
+ @Override
+ public void init(HoltWintersDetectorSpec spec, InputDataFetcher dataFetcher) {
+ this.period = spec.getPeriod();
+ this.alpha = spec.getAlpha();
+ this.beta = spec.getBeta();
+ this.gamma = spec.getGamma();
+ this.dataFetcher = dataFetcher;
+ this.pattern = spec.getPattern();
+ this.smoothing = spec.getSmoothing();
+ this.sensitivity = spec.getSensitivity();
+ }
+
+ @Override
+ public TimeSeries computePredictedTimeSeries(MetricSlice slice) {
+ MetricEntity metricEntity = MetricEntity.fromSlice(slice, 0);
+ Interval window = new Interval(slice.getStart(), slice.getEnd());
+ DateTime trainStart = window.getStart().minus(lookbackPeriod);
+ DatasetConfigDTO datasetConfig = this.dataFetcher.fetchData(new InputDataSpec()
+ .withMetricIdsForDataset(Collections.singleton(metricEntity.getId()))).getDatasetForMetricId()
+ .get(metricEntity.getId());
+
+ DataFrame inputDf = fetchData(metricEntity, trainStart.getMillis(), window.getEndMillis());
+ DataFrame resultDF = computePredictionInterval(inputDf, window.getStartMillis(), datasetConfig.getTimezone());
+
+ // Exclude the end because baseline calculation should not contain the end
+ if (resultDF.size() > 1) {
+ resultDF = resultDF.head(resultDF.size() - 1);
+ }
+
+ return TimeSeries.fromDataFrame(resultDF);
+ }
+
+ @Override
+ public List<MergedAnomalyResultDTO> runDetection(Interval window, String metricUrn) {
+ MetricEntity metricEntity = MetricEntity.fromURN(metricUrn);
+ DateTime trainStart = window.getStart().minus(lookbackPeriod);
+ DatasetConfigDTO datasetConfig = this.dataFetcher.fetchData(new InputDataSpec()
+ .withMetricIdsForDataset(Collections.singleton(metricEntity.getId()))).getDatasetForMetricId()
+ .get(metricEntity.getId());
+ MetricSlice sliceData = MetricSlice.from(metricEntity.getId(), trainStart.getMillis(), window.getEndMillis(),
+ metricEntity.getFilters());
+ DataFrame dfInput = fetchData(metricEntity, trainStart.getMillis(), window.getEndMillis());
+
+
+ // Kernel smoothing
+ if (smoothing && !TimeUnit.DAYS.equals(datasetConfig.bucketTimeGranularity().getUnit())) {
+ int kernelSize = (int) (KERNEL_PERIOD / datasetConfig.bucketTimeGranularity().toMillis());
+ if (kernelSize > 1) {
+ int kernelOffset = kernelSize / 2;
+ double[] values = dfInput.getDoubles(COL_VALUE).values();
+ for (int i = 0; i <= values.length - kernelSize; i++) {
+ values[i + kernelOffset] = AlgorithmUtils.robustMean(dfInput.getDoubles(COL_VALUE)
+ .slice(i, i + kernelSize), kernelSize).getDouble(kernelSize - 1);
+ }
+ dfInput.addSeries(COL_VALUE, values);
+ }
+ }
+
+ DataFrame dfCurr = new DataFrame(dfInput).renameSeries(COL_VALUE, COL_CURR);
+ DataFrame dfBase = computePredictionInterval(dfInput, window.getStartMillis(), datasetConfig.getTimezone())
+ .renameSeries(COL_VALUE, COL_BASE);
+ DataFrame df = new DataFrame(dfCurr).addSeries(dfBase);
+ df.addSeries(COL_DIFF, df.getDoubles(COL_CURR).subtract(df.get(COL_BASE)));
+ df.addSeries(COL_ANOMALY, BooleanSeries.fillValues(df.size(), false));
+
+ // Filter pattern
+ if (pattern.equals(Pattern.UP_OR_DOWN) ) {
+ df.addSeries(COL_PATTERN, BooleanSeries.fillValues(df.size(), true));
+ } else {
+ df.addSeries(COL_PATTERN, pattern.equals(Pattern.UP) ? df.getDoubles(COL_DIFF).gt(0) :
+ df.getDoubles(COL_DIFF).lt(0));
+ }
+ df.addSeries(COL_DIFF_VIOLATION, df.getDoubles(COL_DIFF).abs().gte(df.getDoubles(COL_ERROR)));
+ df.mapInPlace(BooleanSeries.ALL_TRUE, COL_ANOMALY, COL_PATTERN, COL_DIFF_VIOLATION);
+
+ // Anomalies
+ List<MergedAnomalyResultDTO> results = DetectionUtils.makeAnomalies(sliceData, df, COL_ANOMALY,
+ window.getEndMillis(),
+ DetectionUtils.getMonitoringGranularityPeriod(MetricSlice.NATIVE_GRANULARITY.toAggregationGranularityString(),
+ datasetConfig), datasetConfig);
+
+ return results;
+ }
+
+ /**
+ * Fetch data from metric
+ *
+ * @param metricEntity metric entity
+ * @param start start timestamp
+ * @param end end timestamp
+ * @return Data Frame that has data from start to end
+ */
+ private DataFrame fetchData(MetricEntity metricEntity, long start, long end) {
+
+ List<MetricSlice> slices = new ArrayList<>();
+ MetricSlice sliceData = MetricSlice.from(metricEntity.getId(), start, end,
+ metricEntity.getFilters(), MetricSlice.NATIVE_GRANULARITY);
+ slices.add(sliceData);
+ LOG.info("Getting data for" + sliceData.toString());
+ InputData data = this.dataFetcher.fetchData(new InputDataSpec().withTimeseriesSlices(slices)
+ .withMetricIdsForDataset(Collections.singletonList(metricEntity.getId())));
+
+ return data.getTimeseries().get(sliceData);
+ }
+
+ /**
+ * Returns a data frame containing the same time daily data, based on input time
+ * @param originalDF the original dataframe
+ * @param time the epoch time of the start of the day
+ * @return DataFrame containing same time of daily data for LOOKBACK number of days
+ */
+ private DataFrame getDailyDF(DataFrame originalDF, Long time, String timezone) {
+ LongSeries longSeries = (LongSeries) originalDF.get(COL_TIME);
+ long start = longSeries.getLong(0);
+ DateTime dt = new DateTime(time).withZone(DateTimeZone.forID(timezone));
+ DataFrame df = DataFrame.builder(COL_TIME, COL_VALUE).build();
+
+ for (int i = 0; i < LOOKBACK; i++) {
+ DateTime subDt = dt.minusDays(1);
+ long t = subDt.getMillis();
+ if (t < start) {
+ break;
+ }
+ int index = longSeries.find(t);
+ if (index != -1) {
+ df = df.append(originalDF.slice(index, index + 1));
+ } else {
+ int backtrackCounter = 0;
+ // If the 1 day look back data doesn't exist, use the data one period before till backtrackCounter greater than 4
+ while (index == -1 && backtrackCounter <= 4) {
+ subDt = subDt.minusDays(period);
+ long timestamp = subDt.getMillis();
+ index = longSeries.find(timestamp);
+ backtrackCounter++;
+ }
+
+ if (index != -1) {
+ df = df.append(originalDF.slice(index, index + 1));
+ } else {
+ // If not found value up to 4 weeks, insert the last value
+ double lastVal = (originalDF.get(COL_VALUE)).getDouble(longSeries.find(dt.getMillis()));
+ DateTime nextDt = dt.minusDays(1);
+ DataFrame appendDf = DataFrame.builder(COL_TIME, COL_VALUE).append(nextDt, lastVal).build();
+ df = df.append(appendDf);
+ }
+ }
+ dt = dt.minusDays(1);
+ }
+ df = df.reverse();
+ return df;
+ }
+
+ private static double calculateInitialLevel(double[] y) {
+ return y[0];
+ }
+
+ /**
+ * See: http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc435.htm
+ *
+ * @return - Initial trend - Bt[1]
+ */
+ private static double calculateInitialTrend(double[] y, int period) {
+ double sum = 0;
+
+ for (int i = 0; i < period; i++) {
+ sum += y[period + i] - y[i];
+ }
+
+ return sum / (period * period);
+ }
+
+ /**
+ * See: http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc435.htm
+ *
+ * @return - Seasonal Indices.
+ */
+ private static double[] calculateSeasonalIndices(double[] y, int period,
+ int seasons) {
+ double[] seasonalMean = new double[seasons];
+ double[] seasonalIndices = new double[period];
+
+ double[] averagedObservations = new double[y.length];
+
+ for (int i = 0; i < seasons; i++) {
+ for (int j = 0; j < period; j++) {
+ seasonalMean[i] += y[(i * period) + j];
+ }
+ seasonalMean[i] /= period;
+ }
+
+ for (int i = 0; i < seasons; i++) {
+ for (int j = 0; j < period; j++) {
+ averagedObservations[(i * period) + j] = y[(i * period) + j]
+ / seasonalMean[i];
+ }
+ }
+
+ for (int i = 0; i < period; i++) {
+ for (int j = 0; j < seasons; j++) {
+ seasonalIndices[i] += averagedObservations[(j * period) + i];
+ }
+ seasonalIndices[i] /= seasons;
+ }
+
+ return seasonalIndices;
+ }
+
+ /**
+ * Holt Winters forecasting method
+ *
+ * @param y Timeseries to be forecasted
+ * @param alpha level smoothing factor
+ * @param beta trend smoothing factor
+ * @param gamma seasonality smoothing factor
+ * @return ForecastResults containing predicted value, SSE(sum of squared error) and error bound
+ */
+ private ForecastResults forecast(double[] y, double alpha, double beta, double gamma) {
+ double[] seasonal = new double[y.length+1];
+ double[] forecast = new double[y.length+1];
+
+ double a0 = calculateInitialLevel(y);
+ double b0 = calculateInitialTrend(y, period);
+
+ int seasons = y.length / period;
+ double[] initialSeasonalIndices = calculateSeasonalIndices(y, period,
+ seasons);
+
+ for (int i = 0; i < period; i++) {
+ seasonal[i] = initialSeasonalIndices[i];
+ }
+
+ // s is level and t is trend
+ double s = a0;
+ double t = b0;
+ double predictedValue = 0;
+
+ for (int i = 0; i < y.length; i++) {
+ double sNew;
+ double tNew;
+ forecast[i] = (s + t) * seasonal[i];
+ sNew = alpha * (y[i] / seasonal[i]) + (1 - alpha) * (s + t);
+ tNew = beta * (sNew - s) + (1 - beta) * t;
+ if (i + period <= y.length) {
+ seasonal[i + period] = gamma * (y[i] / (sNew * seasonal[i])) + (1 - gamma) * seasonal[i];
+ }
+ s = sNew;
+ t = tNew;
+ if (i == y.length - 1) {
+ predictedValue = (s + t) * seasonal[i+1];
+ }
+ }
+
+ List<Double> diff = new ArrayList<>();
+ double sse = 0;
+ for (int i = 0; i < y.length; i++) {
+ if (forecast[i] != 0) {
+ sse += Math.pow(y[i] - forecast[i], 2);
+ diff.add(Math.abs(forecast[i] - y[i]));
+ }
+ }
+
+ double error = calculateErrorBound(diff, sensitivityToZscore(sensitivity));
+ return new ForecastResults(predictedValue, sse, error);
+ }
+
+ /**
+ * Compute the baseline and error bound for given data
+ *
+ * @param inputDF training dataframe
+ * @param windowStartTime prediction start time
+ * @return DataFrame with timestamp, baseline, error bound
+ */
+ private DataFrame computePredictionInterval(DataFrame inputDF, long windowStartTime, String timezone) {
+
+ DataFrame resultDF = new DataFrame();
+ DataFrame forecastDF = inputDF.filter(new Series.LongConditional() {
+ @Override
+ public boolean apply(long... values) {
+ return values[0] >= windowStartTime;
+ }
+ }, COL_TIME).dropNull();
+
+ int size = forecastDF.size();
+ double[] resultArray = new double[size];
+ long[] resultTimeArray = new long[size];
+ double[] errorArray = new double[size];
+
+ double lastAlpha = alpha;
+ double lastBeta = beta;
+ double lastGamma = gamma;
+
+ for (int k = 0; k < size; k++) {
+ DataFrame dailyDF = getDailyDF(inputDF, forecastDF.getLong(COL_TIME, k), timezone);
+
+ // We need at least 2 periods of data
+ if (dailyDF.size() < 2 * period) {
+ continue;
+ }
+
+ resultTimeArray[k] = forecastDF.getLong(COL_TIME, k);
+
+ double[] y = dailyDF.getDoubles(COL_VALUE).values();
+ HoltWintersParams params;
+ if (alpha < 0 && beta < 0 && gamma < 0) {
+ params = fitModelWithBOBYQA(y, lastAlpha, lastBeta, lastGamma);
+ } else {
+ params = new HoltWintersParams(alpha, beta, gamma);
+ }
+
+ lastAlpha = params.getAlpha();
+ lastBeta = params.getBeta();
+ lastGamma = params.getGamma();
+
+ ForecastResults result = forecast(y, params.getAlpha(), params.getBeta(), params.getGamma());
+
+ resultArray[k] = result.getPredictedValue();
+ errorArray[k] = result.getErrorBound();
+ }
+
+ resultDF.addSeries(COL_TIME, LongSeries.buildFrom(resultTimeArray));
+ resultDF.setIndex(COL_TIME);
+ resultDF.addSeries(COL_VALUE, DoubleSeries.buildFrom(resultArray));
+ resultDF.addSeries(COL_ERROR, DoubleSeries.buildFrom(errorArray));
+ return resultDF;
+ }
+
+ /**
+ * Returns the error bound of given list based on mean, std and given zscore
+ *
+ * @param givenNumbers double list
+ * @param zscore zscore used to multiply by std
+ * @return the error bound
+ */
+ private static double calculateErrorBound(List<Double> givenNumbers, double zscore) {
+ // calculate the mean value (= average)
+ double sum = 0.0;
+ for (double num : givenNumbers) {
+ sum += num;
+ }
+ double mean = sum / givenNumbers.size();
+
+ // calculate standard deviation
+ double squaredDifferenceSum = 0.0;
+ for (double num : givenNumbers) {
+ squaredDifferenceSum += (num - mean) * (num - mean);
+ }
+ double variance = squaredDifferenceSum / givenNumbers.size();
+ double standardDeviation = Math.sqrt(variance);
+
+ return zscore * standardDeviation;
+ }
+
+ /**
+ * Fit alpha, beta, gamma by optimizing SSE (Sum of squared errors) using BOBYQA
+ * It is a derivative free bound constrained optimization algorithm
+ * https://en.wikipedia.org/wiki/BOBYQA
+ *
+ * @param y the data
+ * @param lastAlpha last alpha value
+ * @param lastBeta last beta value
+ * @param lastGamma last gamma value
+ * @return double array containing fitted alpha, beta and gamma
+ */
+ private HoltWintersParams fitModelWithBOBYQA(double[] y, double lastAlpha, double lastBeta, double lastGamma) {
+ BOBYQAOptimizer optimizer = new BOBYQAOptimizer(7);
+ if (lastAlpha < 0) {
+ lastAlpha = 0.1;
+ }
+ if (lastBeta < 0) {
+ lastBeta = 0.01;
+ }
+ if (lastGamma < 0) {
+ lastGamma = 0.001;
+ }
+ InitialGuess initGuess = new InitialGuess(new double[]{lastAlpha, lastBeta, lastGamma});;
+ MaxIter maxIter = new MaxIter(30000);
+ MaxEval maxEval = new MaxEval(30000);
+ GoalType goal = GoalType.MINIMIZE;
+ ObjectiveFunction objectiveFunction = new ObjectiveFunction(new MultivariateFunction() {
+ public double value(double[] params) {
+ return forecast(y, params[0], params[1], params[2]).getSSE();
+ }
+ });
+ SimpleBounds bounds = new SimpleBounds(new double[]{0.001, 0.001, 0.001}, new double[]{0.999, 0.999, 0.999});
+
+ HoltWintersParams params;
+ try {
+ PointValuePair optimal = optimizer.optimize(objectiveFunction, goal, bounds, initGuess, maxIter, maxEval);
+ params = new HoltWintersParams(optimal.getPoint()[0], optimal.getPoint()[1], optimal.getPoint()[2]);
+ } catch (Exception e) {
+ LOG.error(e.toString());
+ params = new HoltWintersParams(lastAlpha, lastBeta, lastGamma);
+ }
+ return params;
+ }
+
+ /**
+ * Mapping of sensitivity to zscore on range of 1 - 4
+ * @param sensitivity double from 0 to 10
+ * @return zscore
+ */
+ private static double sensitivityToZscore(double sensitivity) {
+ // If out of bound, use boundary sensitivity
+ if (sensitivity < 0) {
+ sensitivity = 0;
+ } else if (sensitivity > 10) {
+ sensitivity = 10;
+ }
+ double z = 1 + 0.3 * (10 - sensitivity);
+ return z;
+ }
+
+ /**
+ * Container class to store holt winters parameters
+ */
+ final static class HoltWintersParams {
+ private final double alpha;
+ private final double beta;
+ private final double gamma;
+
+ HoltWintersParams(double alpha, double beta, double gamma) {
+ this.alpha = alpha;
+ this.beta = beta;
+ this.gamma = gamma;
+ }
+
+ double getAlpha() {
+ return alpha;
+ }
+
+ double getBeta() {
+ return beta;
+ }
+
+ double getGamma() {
+ return gamma;
+ }
+ }
+
+ /**
+ * Container class to store forecasting results
+ */
+ final static class ForecastResults {
+ private final double predictedValue;
+ private final double SSE;
+ private final double errorBound;
+
+ ForecastResults(double predictedValue, double SSE, double errorBound) {
+ this.predictedValue = predictedValue;
+ this.SSE = SSE;
+ this.errorBound = errorBound;
+ }
+
+ double getPredictedValue() {
+ return predictedValue;
+ }
+
+ double getSSE() {
+ return SSE;
+ }
+
+ double getErrorBound() {
+ return errorBound;
+ }
+ }
+}
\ No newline at end of file
diff --git a/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/spec/HoltWintersDetectorSpec.java b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/spec/HoltWintersDetectorSpec.java
new file mode 100644
index 0000000..e38067e
--- /dev/null
+++ b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/spec/HoltWintersDetectorSpec.java
@@ -0,0 +1,89 @@
+/*
+ * 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.
+ */
+package org.apache.pinot.thirdeye.detection.spec;
+
+import org.apache.pinot.thirdeye.detection.Pattern;
+
+
+public class HoltWintersDetectorSpec extends AbstractSpec {
+
+ private double alpha = -1;
+ private double beta = -1;
+ private double gamma = -1;
+ private int period = 7;
+ private double sensitivity = 5;
+ private Pattern pattern = Pattern.UP_OR_DOWN;
+ private boolean smoothing = true;
+
+ public boolean getSmoothing() {
+ return smoothing;
+ }
+
+ public Pattern getPattern() {
+ return pattern;
+ }
+
+ public double getSensitivity() {
+ return sensitivity;
+ }
+
+ public double getAlpha() {
+ return alpha;
+ }
+
+ public double getBeta() {
+ return beta;
+ }
+
+ public double getGamma() {
+ return gamma;
+ }
+
+ public int getPeriod() {
+ return period;
+ }
+
+ public void setAlpha(double alpha) {
+ this.alpha = alpha;
+ }
+
+ public void setBeta(double beta) {
+ this.beta = beta;
+ }
+
+ public void setGamma(double gamma) {
+ this.gamma = gamma;
+ }
+
+ public void setPeriod(int period) {
+ this.period = period;
+ }
+
+ public void setPattern(Pattern pattern) {
+ this.pattern = pattern;
+ }
+
+ public void setSensitivity(double sensitivity) {
+ this.sensitivity = sensitivity;
+ }
+
+ public void setSmoothing(boolean smoothing) {
+ this.smoothing = smoothing;
+ }
+}
diff --git a/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/yaml/CompositePipelineConfigTranslator.java b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/yaml/CompositePipelineConfigTranslator.java
index fd637d2..4d0e42e 100644
--- a/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/yaml/CompositePipelineConfigTranslator.java
+++ b/thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/yaml/CompositePipelineConfigTranslator.java
@@ -179,7 +179,8 @@ public class CompositePipelineConfigTranslator extends YamlDetectionConfigTransl
private static final Set<String> TUNING_OFF_COMPONENTS =
ImmutableSet.of("MIGRATED_ALGORITHM_FILTER", "MIGRATED_ALGORITHM", "MIGRATED_ALGORITHM_BASELINE");
private static final Map<String, String> DETECTOR_TO_BASELINE =
- ImmutableMap.of("ALGORITHM", "ALGORITHM_BASELINE", "MIGRATED_ALGORITHM", "MIGRATED_ALGORITHM_BASELINE");
+ ImmutableMap.of("ALGORITHM", "ALGORITHM_BASELINE", "MIGRATED_ALGORITHM", "MIGRATED_ALGORITHM_BASELINE",
+ "HOLT_WINTERS_RULE", "HOLT_WINTERS_RULE");
private static final Set<String> MOVING_WINDOW_DETECTOR_TYPES = ImmutableSet.of("ALGORITHM", "MIGRATED_ALGORITHM");
private final Map<String, Object> components = new HashMap<>();
diff --git a/thirdeye/thirdeye-pinot/src/test/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetectorTest.java b/thirdeye/thirdeye-pinot/src/test/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetectorTest.java
new file mode 100644
index 0000000..49fca9b
--- /dev/null
+++ b/thirdeye/thirdeye-pinot/src/test/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetectorTest.java
@@ -0,0 +1,159 @@
+/*
+ * 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.
+ *
+ */
+
+package org.apache.pinot.thirdeye.detection.components;
+
+import java.io.InputStreamReader;
+import java.io.Reader;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.TimeUnit;
+import org.apache.pinot.thirdeye.dataframe.DataFrame;
+import org.apache.pinot.thirdeye.dataframe.util.MetricSlice;
+import org.apache.pinot.thirdeye.datalayer.dto.DatasetConfigDTO;
+import org.apache.pinot.thirdeye.datalayer.dto.MergedAnomalyResultDTO;
+import org.apache.pinot.thirdeye.datalayer.dto.MetricConfigDTO;
+import org.apache.pinot.thirdeye.detection.DataProvider;
+import org.apache.pinot.thirdeye.detection.DefaultInputDataFetcher;
+import org.apache.pinot.thirdeye.detection.MockDataProvider;
+import org.apache.pinot.thirdeye.detection.algorithm.AlgorithmUtils;
+import org.apache.pinot.thirdeye.detection.spec.HoltWintersDetectorSpec;
+import org.apache.pinot.thirdeye.detection.spi.model.TimeSeries;
+import org.apache.pinot.thirdeye.rootcause.impl.MetricEntity;
+import org.joda.time.Interval;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import org.testng.Assert;
+import org.testng.annotations.BeforeTest;
+import org.testng.annotations.Test;
+
+import static org.apache.pinot.thirdeye.dataframe.util.DataFrameUtils.*;
+import static org.eclipse.jetty.http.HttpParser.*;
+
+/**
+ * Test class for HoltWinters detector
+ */
+public class HoltWintersDetectorTest {
+ private DataProvider provider;
+
+ @BeforeTest
+ public void setUp() throws Exception {
+ DataFrame dailyData;
+ DataFrame hourlyData;
+ try (Reader dataReader = new InputStreamReader(AlgorithmUtils.class.getResourceAsStream("daily.csv"))) {
+ dailyData = DataFrame.fromCsv(dataReader);
+ dailyData.setIndex(COL_TIME);
+ }
+
+ try (Reader dataReader = new InputStreamReader(AlgorithmUtils.class.getResourceAsStream("hourly.csv"))) {
+ hourlyData = DataFrame.fromCsv(dataReader);
+ hourlyData.setIndex(COL_TIME);
+ }
+
+ MetricConfigDTO dailyMetricConfig = new MetricConfigDTO();
+ dailyMetricConfig.setId(1L);
+ dailyMetricConfig.setName("thirdeye-test-daily");
+ dailyMetricConfig.setDataset("thirdeye-test-dataset-daily");
+
+ DatasetConfigDTO dailyDatasetConfig = new DatasetConfigDTO();
+ dailyDatasetConfig.setTimeUnit(TimeUnit.DAYS);
+ dailyDatasetConfig.setDataset("thirdeye-test-dataset-daily");
+ dailyDatasetConfig.setTimeDuration(1);
+
+ MetricConfigDTO hourlyMetricConfig = new MetricConfigDTO();
+ hourlyMetricConfig.setId(123L);
+ hourlyMetricConfig.setName("thirdeye-test-hourly");
+ hourlyMetricConfig.setDataset("thirdeye-test-dataset-hourly");
+
+ DatasetConfigDTO hourlyDatasetConfig = new DatasetConfigDTO();
+ hourlyDatasetConfig.setTimeUnit(TimeUnit.HOURS);
+ hourlyDatasetConfig.setDataset("thirdeye-test-dataset-hourly");
+ hourlyDatasetConfig.setTimeDuration(1);
+
+ Map<MetricSlice, DataFrame> timeseries = new HashMap<>();
+ timeseries.put(MetricSlice.from(1L, 1301443200000L, 1309219200000L), dailyData);
+ timeseries.put(MetricSlice.from(123L, 1317585600000L, 1323378000000L), hourlyData);
+ // For Travis CI
+ timeseries.put(MetricSlice.from(123L, 1317589200000L, 1323378000000L), hourlyData);
+
+ this.provider = new MockDataProvider()
+ .setTimeseries(timeseries)
+ .setMetrics(Arrays.asList(hourlyMetricConfig, dailyMetricConfig))
+ .setDatasets(Arrays.asList(hourlyDatasetConfig, dailyDatasetConfig));
+ }
+
+ @Test
+ public void testComputePredictedTimeSeriesDaily() {
+ HoltWintersDetector detector = new HoltWintersDetector();
+ HoltWintersDetectorSpec spec = new HoltWintersDetectorSpec();
+ detector.init(spec, new DefaultInputDataFetcher(this.provider, -1));
+ Interval window = new Interval(1306627200000L, 1309219200000L);
+ String metricUrn = "thirdeye:metric:1";
+ MetricEntity me = MetricEntity.fromURN(metricUrn);
+ MetricSlice slice = MetricSlice.from(me.getId(), window.getStartMillis(), window.getEndMillis(), me.getFilters());
+ TimeSeries timeSeries = detector.computePredictedTimeSeries(slice);
+
+ Assert.assertEquals(timeSeries.getPredictedBaseline().size(), 29);
+ }
+
+ @Test
+ public void testRunDetectionDaily() {
+ HoltWintersDetector detector = new HoltWintersDetector();
+ HoltWintersDetectorSpec spec = new HoltWintersDetectorSpec();
+ spec.setSensitivity(8);
+ detector.init(spec, new DefaultInputDataFetcher(this.provider, -1));
+ Interval window = new Interval(1306627200000L, 1309219200000L);
+ String metricUrn = "thirdeye:metric:1";
+ List<MergedAnomalyResultDTO> anomalies = detector.runDetection(window, metricUrn);
+
+ Assert.assertEquals(anomalies.size(), 6);
+ }
+
+ @Test
+ public void testComputePredictedTimeSeriesHourly() {
+ HoltWintersDetector detector = new HoltWintersDetector();
+ HoltWintersDetectorSpec spec = new HoltWintersDetectorSpec();
+ detector.init(spec, new DefaultInputDataFetcher(this.provider, -1));
+
+ Interval window = new Interval(1322773200000L, 1323378000000L);
+
+ String metricUrn = "thirdeye:metric:123";
+ MetricEntity me = MetricEntity.fromURN(metricUrn);
+ MetricSlice slice = MetricSlice.from(me.getId(), window.getStartMillis(), window.getEndMillis(), me.getFilters());
+ TimeSeries timeSeries = detector.computePredictedTimeSeries(slice);
+
+ Assert.assertEquals(timeSeries.getPredictedBaseline().size(), 167);
+ }
+
+ @Test
+ public void testRunDetectionHourly() {
+ HoltWintersDetector detector = new HoltWintersDetector();
+ HoltWintersDetectorSpec spec = new HoltWintersDetectorSpec();
+ spec.setSensitivity(8);
+ detector.init(spec, new DefaultInputDataFetcher(this.provider, -1));
+ Interval window = new Interval(1322773200000L, 1323378000000L);
+ String metricUrn = "thirdeye:metric:123";
+ List<MergedAnomalyResultDTO> anomalies = detector.runDetection(window, metricUrn);
+
+ Assert.assertEquals(anomalies.size(), 3);
+ }
+}
\ No newline at end of file
diff --git a/thirdeye/thirdeye-pinot/src/test/java/org/apache/pinot/thirdeye/detection/components/PercentageChangeRuleDetectorTest.java b/thirdeye/thirdeye-pinot/src/test/java/org/apache/pinot/thirdeye/detection/components/PercentageChangeRuleDetectorTest.java
index 9ca76a6..9bbe29b 100644
--- a/thirdeye/thirdeye-pinot/src/test/java/org/apache/pinot/thirdeye/detection/components/PercentageChangeRuleDetectorTest.java
+++ b/thirdeye/thirdeye-pinot/src/test/java/org/apache/pinot/thirdeye/detection/components/PercentageChangeRuleDetectorTest.java
@@ -185,6 +185,5 @@ public class PercentageChangeRuleDetectorTest {
Assert.assertEquals(anomalies.size(), 1);
Assert.assertEquals(anomalies.get(0).getStartTime(), 1551484800000L);
Assert.assertEquals(anomalies.get(0).getEndTime(), 1551488400000L);
-
}
}
diff --git a/thirdeye/thirdeye-pinot/src/test/resources/org/apache/pinot/thirdeye/detection/algorithm/daily.csv b/thirdeye/thirdeye-pinot/src/test/resources/org/apache/pinot/thirdeye/detection/algorithm/daily.csv
new file mode 100644
index 0000000..fc0ace4
--- /dev/null
+++ b/thirdeye/thirdeye-pinot/src/test/resources/org/apache/pinot/thirdeye/detection/algorithm/daily.csv
@@ -0,0 +1,732 @@
+timestamp,value
+1293840000000,985
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diff --git a/thirdeye/thirdeye-pinot/src/test/resources/org/apache/pinot/thirdeye/detection/algorithm/hourly.csv b/thirdeye/thirdeye-pinot/src/test/resources/org/apache/pinot/thirdeye/detection/algorithm/hourly.csv
new file mode 100644
index 0000000..dc748f5
--- /dev/null
+++ b/thirdeye/thirdeye-pinot/src/test/resources/org/apache/pinot/thirdeye/detection/algorithm/hourly.csv
@@ -0,0 +1,3000 @@
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