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Enterprise only

Gives an anomaly score to each point using a left matrix profile algorithm1. To make the matrix profile approach better at point anomaly detection, a proprietary transformation function is applied to the matrix profile.

This detector returns an anomaly score, it does not return expected values, upper and lower bounds. This detector is recommended for granularity <= 1 hour.


"targetProperty": "current": The data on which to perform detection. It should contain the historical data to use for training.


namedescriptiondefault value
component.sensitivityAnomaly score threshold. Eg for 0.8`` every point that has as a score bigger that 0.8` is flagged as an anomaly.
component.lookbackPeriodHistorical period to use as a reference. In ISO-8601 (opens in a new tab) format. Requires component.monitoringGranularity, see shared parameters. Eg: P14D. If component.lookbackPeriod is not set, component.lookback is used.
component.seasonalityPeriodSeasonality biggest period. Used to infer the window size of the matrix profile. In ISO-8601 format. Requires component.monitoringGranularity, see shared parameters. Eg: P7D.null (inferred from the data)
component.distanceFor advanced users. Whether to use NORMALIZED or NON_NORMALIZED euclidean distance for the matrix profile computation.NORMALIZED
component.computeBoundsExperimental. If true, attempts to compute expected values, upper and lower bounds.false
component.scoringMethodExperimental. Experimental. Post-processing of the matrix profile anomaly scores. FIRST_ORDER_DIFFERENCE is good at detecting spikes. DIRECT is good at detecting drifts.FIRST_ORDER_DIFFERENCE


Recommended defaults

      "name": "root",
      "type": "AnomalyDetector",
      "params": {
        "type": "MATRIX_PROFILE",
        "component.sensitivity": "2",
        "component.lookbackPeriod": "P21D",
        ...  # shared parameters
      "inputs": [
          "targetProperty": "current",
          "sourcePlanNode": "currentData",
          "sourceProperty": "currentOutput"
      "outputs": []


  1. See (opens in a new tab)