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Inputs
"targetProperty": "current": The data on which to perform detection. It should contain the historical data to use for training.
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"targetProperty": "current": The data on which to perform detection. It should contain the historical data to use for training.
| name | description | default value |
|---|---|---|
component.sensitivity | Anomaly score threshold. Eg for 0.8`` every point that has as a score bigger that 0.8` is flagged as an anomaly. | |
component.lookbackPeriod | Historical period to use as a reference. In [ISO-8601][iso-8601] format. Requires component.monitoringGranularity, see shared parameters. Eg: P14D. If component.lookbackPeriod is not set, component.lookback is used. | |
component.seasonalityPeriod | Seasonality 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.distance | For advanced users. Whether to use NORMALIZED or NON_NORMALIZED euclidean distance for the matrix profile computation. | NORMALIZED |
component.computeBounds | Experimental. If true, attempts to compute expected values, upper and lower bounds. | false |
component.scoringMethod | Experimental. 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 |
{
"name": "root",
"type": "AnomalyDetector",
"params": {
"type": "MATRIX_PROFILE",
"component.sensitivity": "2",
"component.lookbackPeriod": "P21D",
... # shared parameters
},
"inputs": [
{
"targetProperty": "current",
"sourcePlanNode": "currentData",
"sourceProperty": "currentOutput"
}
],
"outputs": []
}
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