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

Example 16 with TimeSeries

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

the class SeasonalAveragePredictionModel method train.

@Override
public void train(List<TimeSeries> baselineTimeSeries, AnomalyDetectionContext anomalyDetectionContext) {
    expectedTimeSeries = new TimeSeries();
    if (CollectionUtils.isNotEmpty(baselineTimeSeries)) {
        TimeSeries baseTimeSeries = getLatestTimeSeries(baselineTimeSeries);
        Interval baseInterval = baseTimeSeries.getTimeSeriesInterval();
        long bucketSizeInMillis = anomalyDetectionContext.getBucketSizeInMS();
        long baseStart = baseInterval.getStartMillis();
        long baseEnd = baseInterval.getEndMillis();
        int bucketCount = (int) ((baseEnd - baseStart) / bucketSizeInMillis);
        expectedTimeSeries.setTimeSeriesInterval(baseInterval);
        if (baselineTimeSeries.size() > 1) {
            for (int i = 0; i < bucketCount; ++i) {
                double sum = 0d;
                int count = 0;
                long timeOffset = i * bucketSizeInMillis;
                for (TimeSeries ts : baselineTimeSeries) {
                    long timestamp = ts.getTimeSeriesInterval().getStartMillis() + timeOffset;
                    Double value = ts.get(timestamp);
                    if (value != null) {
                        sum += value;
                        ++count;
                    }
                }
                if (count != 0) {
                    long timestamp = baseStart + timeOffset;
                    double avgValue = sum / (double) count;
                    expectedTimeSeries.set(timestamp, avgValue);
                }
            }
        } else {
            for (int i = 0; i < bucketCount; ++i) {
                long timestamp = baseStart + i * bucketSizeInMillis;
                Double value = baseTimeSeries.get(timestamp);
                if (value != null) {
                    expectedTimeSeries.set(timestamp, value);
                }
            }
        }
    }
}

Example 17 with TimeSeries

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

the class TotalCountThresholdRemovalFunction method transform.

/**
   * Returns an empty time series if the sum of the total count metric does not exceed the
   * threshold.
   *
   * @param timeSeries              the time series that provides the data points to be
   *                                transformed.
   * @param anomalyDetectionContext the anomaly detection context that could provide additional
   *                                information for the transformation. Specifically, the time
   *                                series that provide the values to compute the total count. Note
   *                                that this function simply sum up the values of the specified
   *                                time series and hence the timestamp of that time series does not
   *                                matter. Moreover, this metric has to be put in the set of
   *                                current time series.
   *
   * @return the original time series the sum of the values in total count time series exceeds the
   * threshold.
   */
@Override
public TimeSeries transform(TimeSeries timeSeries, AnomalyDetectionContext anomalyDetectionContext) {
    String totalCountMetricName = getProperties().getProperty(TOTAL_COUNT_METRIC_NAME);
    if (StringUtils.isBlank(totalCountMetricName)) {
        return timeSeries;
    }
    double totalCountThreshold = Double.valueOf(getProperties().getProperty(TOTAL_COUNT_THRESHOLD, "0"));
    TimeSeries totalCountTS = anomalyDetectionContext.getCurrent(totalCountMetricName);
    double sum = 0d;
    for (long timestamp : totalCountTS.timestampSet()) {
        sum += totalCountTS.get(timestamp);
    }
    if (Double.compare(sum, totalCountThreshold) < 0) {
        TimeSeries emptyTimeSeries = new TimeSeries();
        emptyTimeSeries.setTimeSeriesInterval(timeSeries.getTimeSeriesInterval());
        return emptyTimeSeries;
    } else {
        return timeSeries;
    }
}

Example 18 with TimeSeries

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

the class TestMinMaxThresholdFunction method recomputeMergedAnomalyWeight.

@Test(dataProvider = "timeSeriesDataProvider")
public void recomputeMergedAnomalyWeight(Properties properties, TimeSeriesKey timeSeriesKey, long bucketSizeInMs, TimeSeries observedTimeSeries) throws Exception {
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    properties.put(MinMaxThresholdDetectionModel.MAX_VAL, "20");
    properties.put(MinMaxThresholdDetectionModel.MIN_VAL, "12");
    // Create anomaly function spec
    AnomalyFunctionDTO functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(TestWeekOverWeekRuleFunction.toString(properties));
    AnomalyDetectionFunction function = new MinMaxThresholdFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    List<RawAnomalyResultDTO> expectedRawAnomalies = new ArrayList<>();
    RawAnomalyResultDTO rawAnomaly1 = new RawAnomalyResultDTO();
    rawAnomaly1.setStartTime(observedStartTime + bucketMillis * 3);
    rawAnomaly1.setEndTime(observedStartTime + bucketMillis * 4);
    rawAnomaly1.setWeight(0.1d);
    rawAnomaly1.setScore(13.6d);
    expectedRawAnomalies.add(rawAnomaly1);
    RawAnomalyResultDTO rawAnomaly2 = new RawAnomalyResultDTO();
    rawAnomaly2.setStartTime(observedStartTime + bucketMillis * 4);
    rawAnomaly2.setEndTime(observedStartTime + bucketMillis * 5);
    rawAnomaly2.setWeight(-0.33333d);
    rawAnomaly2.setScore(13.6d);
    expectedRawAnomalies.add(rawAnomaly2);
    MergedAnomalyResultDTO mergedAnomaly = new MergedAnomalyResultDTO();
    mergedAnomaly.setStartTime(expectedRawAnomalies.get(0).getStartTime());
    mergedAnomaly.setEndTime(expectedRawAnomalies.get(1).getEndTime());
    mergedAnomaly.setAnomalyResults(expectedRawAnomalies);
    function.updateMergedAnomalyInfo(anomalyDetectionContext, mergedAnomaly);
    double currentTotal = 0d;
    double deviationFromThreshold = 0d;
    Interval interval = new Interval(mergedAnomaly.getStartTime(), mergedAnomaly.getEndTime());
    TimeSeries currentTS = anomalyDetectionContext.getTransformedCurrent(mainMetric);
    for (long timestamp : currentTS.timestampSet()) {
        if (interval.contains(timestamp)) {
            double value = currentTS.get(timestamp);
            currentTotal += value;
            deviationFromThreshold += computeDeviationFromMinMax(value, 12d, 20d);
        }
    }
    double score = currentTotal / 2d;
    double weight = deviationFromThreshold / 2d;
    Assert.assertEquals(mergedAnomaly.getScore(), score, EPSILON);
    Assert.assertEquals(mergedAnomaly.getAvgCurrentVal(), score, EPSILON);
    Assert.assertEquals(mergedAnomaly.getWeight(), weight, EPSILON);
}

Example 19 with TimeSeries

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

the class TestMinMaxThresholdFunction 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, 22d);
        observedTimeSeries.set(observedStartTime + bucketMillis * 4, 8d);
        Interval observedTimeSeriesInterval = new Interval(observedStartTime, observedStartTime + bucketMillis * 5);
        observedTimeSeries.setTimeSeriesInterval(observedTimeSeriesInterval);
    }
    return new Object[][] { { properties, timeSeriesKey, bucketSizeInMS, observedTimeSeries } };
}