tangdian commented on a change in pull request #4067: [TE] Holt Winters detector URL: https://github.com/apache/incubator-pinot/pull/4067#discussion_r275880161
########## File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetector.java ########## @@ -0,0 +1,541 @@ +/* + * 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.common.time.TimeGranularity; +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.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 TimeGranularity timeGranularity; + private double sensitivity; + private String monitoringGranularity; + 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(); + this.monitoringGranularity = spec.getMonitoringGranularity(); + if (this.monitoringGranularity.equals("1_MONTHS")) { + this.timeGranularity = MetricSlice.NATIVE_GRANULARITY; + } else { + this.timeGranularity = TimeGranularity.fromString(spec.getMonitoringGranularity()); + } + } + + @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); + DataFrame inputDf = fetchData(metricEntity, trainStart.getMillis(), window.getEndMillis()); + DataFrame resultDF = computePredictionInterval(inputDf, window.getStartMillis()); + + // 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(), timeGranularity); + 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()).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(monitoringGranularity, 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(), timeGranularity); + 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) { + LongSeries longSeries = (LongSeries) originalDF.get(COL_TIME); + long start = longSeries.getLong(0); + DateTime dt = new DateTime(time); + DataFrame df = DataFrame.builder(COL_TIME, COL_VALUE).build(); + + for (int i = 0; i < LOOKBACK; i++) { + DateTime subDt = new DateTime(dt); + subDt = subDt.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 { // If the 1 day look back data doesn't exist, use the data one period before + int backtrackCounter = 0; // Counter for how many periods we have searched for + while (index == -1) { // Stop when we found data + subDt = subDt.minusDays(period); + long tWO1W = subDt.getMillis(); + index = longSeries.find(tWO1W); + if (index != -1) { + df = df.append(originalDF.slice(index, index + 1)); + } + if (backtrackCounter > 4) { // Search up to 4 weeks, otherwise just append last value to next time frame + double lastVal = (originalDF.get(COL_VALUE)).getDouble(longSeries.find(dt.getMillis())); // The last value + DateTime nextDt = dt.minusDays(1); // The next datetime to be appended + DataFrame appendDf = DataFrame.builder(COL_TIME, COL_VALUE).append(nextDt, lastVal).build(); + df = df.append(appendDf); + break; + } + backtrackCounter++; + } + } + 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[] seasonalAverage = 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++) { + seasonalAverage[i] += y[(i * period) + j]; + } + seasonalAverage[i] /= period; + } + + for (int i = 0; i < seasons; i++) { + for (int j = 0; j < period; j++) { + averagedObservations[(i * period) + j] = y[(i * period) + j] + / seasonalAverage[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 double[] {y_hat, SSE, error_boundary} + */ + private double[] forecast(double[] y, double alpha, double beta, double gamma) { + double[] It = new double[y.length+1]; + double[] Ft = 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++) { + It[i] = initialSeasonalIndices[i]; + } + + double s = a0; + double t = b0; + double predictedValue = 0; + + for (int i = 0; i < y.length; i++) { + double sNew; + double tNew; + Ft[i] = (s + t) * It[i]; + sNew = alpha * (y[i] / It[i]) + (1 - alpha) * (s + t); + tNew = beta * (sNew - s) + (1 - beta) * t; + if (i + period <= y.length) { + It[i + period] = gamma * (y[i] / (sNew * It[i])) + (1 - gamma) * It[i]; + } + s = sNew; + t = tNew; + if (i == y.length - 1) { + predictedValue = (s+t) * It[i+1]; + } + } + + List<Double> diff = new ArrayList<>(); + double sse = 0; + for (int i = 0; i < y.length; i++) { + if (Ft[i] != 0) { + sse += Math.pow(y[i] - Ft[i], 2); + diff.add(Math.abs(Ft[i] - y[i])); + } + } + + double error = calculateErrorBound(diff, sensitivityToZscore(sensitivity)); + return new double[]{predictedValue, sse, error}; + } + + /** + * + * @param inputDF training dataframe + * @param windowStartTime prediction start time + * @return DataFrame with timestamp, baseline, error bound + */ + private DataFrame computePredictionInterval(DataFrame inputDF, long windowStartTime) { + + 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 = this.alpha; Review comment: Good point, because I accidentally deleted some code down below so this didn't make sense. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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