Examples with TimeSeries com.linkedin.thirdeye.anomalydetection.context.TimeSeries used on opensource projects

Example 11 with TimeSeries

use of com.linkedin.thirdeye.anomalydetection.context.TimeSeries in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method analyzeWoW.

@Test(dataProvider = "timeSeriesDataProvider")
public void analyzeWoW(Properties properties, TimeSeriesKey timeSeriesKey, long bucketSizeInMs, TimeSeries observedTimeSeries, List<TimeSeries> baselines) throws Exception {
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    // Append properties for anomaly function specific setting
    properties.put(WeekOverWeekRuleFunction.BASELINE, "w/w");
    properties.put(SimpleThresholdDetectionModel.CHANGE_THRESHOLD, "-0.2");
    // Create anomaly function spec
    AnomalyFunctionDTO functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(toString(properties));
    WeekOverWeekRuleFunction function = new WeekOverWeekRuleFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    List<TimeSeries> singleBaseline = new ArrayList<>();
    singleBaseline.add(baselines.get(0));
    anomalyDetectionContext.setBaselines(mainMetric, singleBaseline);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    List<RawAnomalyResultDTO> rawAnomalyResults = function.analyze(anomalyDetectionContext);
    compareWoWRawAnomalies(rawAnomalyResults);
    // Test data model
    List<Interval> expectedDataRanges = new ArrayList<>();
    expectedDataRanges.add(new Interval(observedStartTime, observedStartTime + bucketMillis * 5));
    expectedDataRanges.add(new Interval(observedStartTime - oneWeekInMillis, observedStartTime + bucketMillis * 5 - oneWeekInMillis));
    List<Interval> actualDataRanges = function.getDataModel().getAllDataIntervals(observedStartTime, observedStartTime + bucketMillis * 5);
    Assert.assertEquals(actualDataRanges, expectedDataRanges);
}

Example 12 with TimeSeries

use of com.linkedin.thirdeye.anomalydetection.context.TimeSeries in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method recomputeMergedAnomalyWeight.

@Test(dataProvider = "timeSeriesDataProvider")
public void recomputeMergedAnomalyWeight(Properties properties, TimeSeriesKey timeSeriesKey, long bucketSizeInMs, TimeSeries observedTimeSeries, List<TimeSeries> baselines) throws Exception {
    // Expected RawAnomalies without smoothing
    List<RawAnomalyResultDTO> expectedRawAnomalies = new ArrayList<>();
    RawAnomalyResultDTO rawAnomaly1 = new RawAnomalyResultDTO();
    rawAnomaly1.setStartTime(observedStartTime + bucketMillis * 2);
    rawAnomaly1.setEndTime(observedStartTime + bucketMillis * 3);
    rawAnomaly1.setWeight(0.3d);
    rawAnomaly1.setScore(15d);
    expectedRawAnomalies.add(rawAnomaly1);
    RawAnomalyResultDTO rawAnomaly2 = new RawAnomalyResultDTO();
    rawAnomaly2.setStartTime(observedStartTime + bucketMillis * 3);
    rawAnomaly2.setEndTime(observedStartTime + bucketMillis * 4);
    rawAnomaly2.setWeight(0.22727272727272727);
    rawAnomaly2.setScore(15d);
    expectedRawAnomalies.add(rawAnomaly2);
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    // Append properties for anomaly function specific setting
    properties.put(WeekOverWeekRuleFunction.BASELINE, "w/2wAvg");
    properties.put(SimpleThresholdDetectionModel.CHANGE_THRESHOLD, "0.2");
    // Create anomaly function spec
    AnomalyFunctionDTO functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(toString(properties));
    WeekOverWeekRuleFunction function = new WeekOverWeekRuleFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setBaselines(mainMetric, baselines);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    MergedAnomalyResultDTO mergedAnomaly = new MergedAnomalyResultDTO();
    mergedAnomaly.setStartTime(expectedRawAnomalies.get(0).getStartTime());
    mergedAnomaly.setEndTime(expectedRawAnomalies.get(1).getEndTime());
    mergedAnomaly.setAnomalyResults(expectedRawAnomalies);
    function.updateMergedAnomalyInfo(anomalyDetectionContext, mergedAnomaly);
    // Test weight; weight is the percentage change between the sums of observed values and
    // expected values, respectively. Note that expected values are generated by the trained model,
    // which takes as input one or many baseline time series.
    final long oneWeekInMillis = TimeUnit.DAYS.toMillis(7);
    double observedTotal = 0d;
    double baselineTotal = 0d;
    int bucketCount = 0;
    Interval interval = new Interval(mergedAnomaly.getStartTime(), mergedAnomaly.getEndTime());
    TimeSeries observedTS = anomalyDetectionContext.getTransformedCurrent(mainMetric);
    List<TimeSeries> baselineTSs = anomalyDetectionContext.getTransformedBaselines(mainMetric);
    for (long timestamp : observedTS.timestampSet()) {
        if (interval.contains(timestamp)) {
            ++bucketCount;
            observedTotal += observedTS.get(timestamp);
            for (int i = 0; i < baselineTSs.size(); ++i) {
                TimeSeries baselineTS = baselineTSs.get(i);
                long baseTimeStamp = timestamp - oneWeekInMillis * (i + 1);
                baselineTotal += baselineTS.get(baseTimeStamp);
            }
        }
    }
    baselineTotal /= baselineTSs.size();
    // Compare anomaly weight, avg. current, avg. baseline, score, etc
    double expectedWeight = (observedTotal - baselineTotal) / baselineTotal;
    Assert.assertEquals(mergedAnomaly.getWeight(), expectedWeight, EPSILON);
    double avgCurrent = observedTotal / bucketCount;
    Assert.assertEquals(mergedAnomaly.getAvgCurrentVal(), avgCurrent, EPSILON);
    double avgBaseline = baselineTotal / bucketCount;
    Assert.assertEquals(mergedAnomaly.getAvgBaselineVal(), avgBaseline, EPSILON);
    // Test Score; score is the average of all raw anomalies' score
    double expectedScore = 0d;
    for (RawAnomalyResultDTO rawAnomaly : expectedRawAnomalies) {
        expectedScore += rawAnomaly.getScore();
    }
    expectedScore /= expectedRawAnomalies.size();
    Assert.assertEquals(mergedAnomaly.getScore(), expectedScore, EPSILON);
}

Example 13 with TimeSeries

use of com.linkedin.thirdeye.anomalydetection.context.TimeSeries in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method timeSeriesDataProvider.

@DataProvider(name = "timeSeriesDataProvider")
public Object[][] timeSeriesDataProvider() {
    // The properties for the testing time series
    Properties properties = new Properties();
    long bucketSizeInMS = TimeUnit.SECONDS.toMillis(1);
    // Set up time series key for the testing time series
    TimeSeriesKey timeSeriesKey = new TimeSeriesKey();
    String metric = mainMetric;
    timeSeriesKey.setMetricName(metric);
    DimensionMap dimensionMap = new DimensionMap();
    dimensionMap.put("dimensionName1", "dimensionValue1");
    dimensionMap.put("dimensionName2", "dimensionValue2");
    timeSeriesKey.setDimensionMap(dimensionMap);
    TimeSeries observedTimeSeries = new TimeSeries();
    {
        observedTimeSeries.set(observedStartTime, 10d);
        observedTimeSeries.set(observedStartTime + bucketMillis, 15d);
        observedTimeSeries.set(observedStartTime + bucketMillis * 2, 13d);
        observedTimeSeries.set(observedStartTime + bucketMillis * 3, 27d);
        observedTimeSeries.set(observedStartTime + bucketMillis * 4, 10d);
        Interval observedTimeSeriesInterval = new Interval(observedStartTime, observedStartTime + bucketMillis * 5);
        observedTimeSeries.setTimeSeriesInterval(observedTimeSeriesInterval);
    }
    List<TimeSeries> baselines = new ArrayList<>();
    TimeSeries baseline1TimeSeries = new TimeSeries();
    {
        baseline1TimeSeries.set(baseline1StartTime, 10d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis, 20d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis * 2, 15d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis * 3, 24d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis * 4, 14d);
        Interval baseline1Interval = new Interval(baseline1StartTime, baseline1StartTime + bucketMillis * 5);
        baseline1TimeSeries.setTimeSeriesInterval(baseline1Interval);
    }
    baselines.add(baseline1TimeSeries);
    TimeSeries baseline2TimeSeries = new TimeSeries();
    {
        baseline2TimeSeries.set(baseline2StartTime, 10d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis, 10d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis * 2, 5d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis * 3, 20d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis * 4, 10d);
        Interval baseline2Interval = new Interval(baseline2StartTime, baseline2StartTime + bucketMillis * 5);
        baseline2TimeSeries.setTimeSeriesInterval(baseline2Interval);
    }
    baselines.add(baseline2TimeSeries);
    return new Object[][] { { properties, timeSeriesKey, bucketSizeInMS, observedTimeSeries, baselines } };
}

Example 14 with TimeSeries

use of com.linkedin.thirdeye.anomalydetection.context.TimeSeries in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method testTotalCountThresholdFunction.

@Test(dataProvider = "timeSeriesDataProvider")
public void testTotalCountThresholdFunction(Properties properties, TimeSeriesKey timeSeriesKey, long bucketSizeInMs, TimeSeries observedTimeSeries, List<TimeSeries> baselines) throws Exception {
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    // Append properties for anomaly function specific setting
    String totalCountTimeSeriesName = "totalCount";
    TimeSeries totalCountTimeSeries = new TimeSeries();
    {
        totalCountTimeSeries.set(observedStartTime, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis * 2, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis * 3, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis * 4, 10d);
        Interval totalCountTimeSeriesInterval = new Interval(observedStartTime, observedStartTime + bucketMillis * 5);
        totalCountTimeSeries.setTimeSeriesInterval(totalCountTimeSeriesInterval);
    }
    properties.put(TotalCountThresholdRemovalFunction.TOTAL_COUNT_METRIC_NAME, totalCountTimeSeriesName);
    properties.put(TotalCountThresholdRemovalFunction.TOTAL_COUNT_THRESHOLD, "51");
    properties.put(WeekOverWeekRuleFunction.BASELINE, "w/2wAvg");
    properties.put(SimpleThresholdDetectionModel.CHANGE_THRESHOLD, "0.2");
    // Create anomaly function spec
    AnomalyFunctionDTO functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(toString(properties));
    // Create anomalyDetectionContext using anomaly function spec
    WeekOverWeekRuleFunction function = new WeekOverWeekRuleFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setBaselines(mainMetric, baselines);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    anomalyDetectionContext.setCurrent(totalCountTimeSeriesName, totalCountTimeSeries);
    List<RawAnomalyResultDTO> rawAnomalyResults = function.analyze(anomalyDetectionContext);
    // No anomalies after smoothing the time series
    Assert.assertEquals(rawAnomalyResults.size(), 0);
    // Test disabled total count by lowering the threshold
    anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    properties.put(TotalCountThresholdRemovalFunction.TOTAL_COUNT_THRESHOLD, "0");
    // Create anomaly function spec
    functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(toString(properties));
    function = new WeekOverWeekRuleFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setBaselines(mainMetric, baselines);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    anomalyDetectionContext.setCurrent(totalCountTimeSeriesName, totalCountTimeSeries);
    rawAnomalyResults = function.analyze(anomalyDetectionContext);
    compareWo2WAvgRawAnomalies(rawAnomalyResults);
}

Example 15 with TimeSeries

use of com.linkedin.thirdeye.anomalydetection.context.TimeSeries in project pinot by linkedin.

the class MinMaxThresholdMergeModel method update.

@Override
public void update(AnomalyDetectionContext anomalyDetectionContext, MergedAnomalyResultDTO anomalyToUpdated) {
    // Get min / max props
    Properties props = getProperties();
    Double min = null;
    if (props.containsKey(MIN_VAL)) {
        min = Double.valueOf(props.getProperty(MIN_VAL));
    }
    Double max = null;
    if (props.containsKey(MAX_VAL)) {
        max = Double.valueOf(props.getProperty(MAX_VAL));
    }
    String metricName = anomalyDetectionContext.getAnomalyDetectionFunction().getSpec().getTopicMetric();
    TimeSeries timeSeries = anomalyDetectionContext.getTransformedCurrent(metricName);
    Interval timeSeriesInterval = timeSeries.getTimeSeriesInterval();
    long windowStartInMillis = timeSeriesInterval.getStartMillis();
    long windowEndInMillis = timeSeriesInterval.getEndMillis();
    double currentAverageValue = 0d;
    int currentBucketCount = 0;
    double deviationFromThreshold = 0d;
    long anomalyStartTime = anomalyToUpdated.getStartTime();
    long anomalyEndTime = anomalyToUpdated.getEndTime();
    Interval anomalyInterval = new Interval(anomalyStartTime, anomalyEndTime);
    for (long time : timeSeries.timestampSet()) {
        if (anomalyInterval.contains(time)) {
            double value = timeSeries.get(time);
            if (value != 0d) {
                if (windowStartInMillis <= time && time <= windowEndInMillis) {
                    currentAverageValue += value;
                    ++currentBucketCount;
                    deviationFromThreshold += MinMaxThresholdDetectionModel.getDeviationFromThreshold(value, min, max);
                }
            // else ignore unknown time key
            }
        }
    }
    if (currentBucketCount != 0d) {
        currentAverageValue /= currentBucketCount;
        deviationFromThreshold /= currentBucketCount;
    }
    anomalyToUpdated.setScore(currentAverageValue);
    anomalyToUpdated.setWeight(deviationFromThreshold);
    anomalyToUpdated.setAvgCurrentVal(currentAverageValue);
    String message = String.format(DEFAULT_MESSAGE_TEMPLATE, deviationFromThreshold, currentAverageValue, min, max);
    anomalyToUpdated.setMessage(message);
}