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HOLT_WINTERS

Compares current value to the value predicted by the Holt-Winters statistic forecasting algorithm1.

Inputs

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

Parameters

namedescriptiondefault value
component.sensitivityDetection sensitivity scale from 0 - 10, mapping z-score from 1 to 3.5
component.lookbackPeriodHistorical period to use for training. In ISO-8601 format. Requires component.monitoringGranularity, see shared parameters. Eg: P14D. If component.lookbackPeriod is not set, component.lookback is used.
component.lookbackDeprecated. Prefer component.lookbackPeriod. Number of data points to use for training.60
component.patternDetect as an anomaly if the metric drop, rise or both directions. UP, DOWN, UP_OR_DOWN.UP_OR_DOWN
component.periodDeprecated. Prefer component.seasonalityPeriod. Seasonality biggest period in number of data points.7
component.seasonalityPeriodSeasonality biggest period to learn. In ISO-8601 format. Requires component.monitoringGranularity, see shared parameters. Eg: P7D will learn weekly and smaller seasonalities. If component.seasonalityPeriod is not set, component.period is used.
component.alphaLevel smoothing factor. In [0,1]-1 - Optimized by BOBYQA
component.betaTrend smoothing factor. In [0,1]-1 - Optimized by BOBYQA
component.gammaSeasonal smoothing factor. In [0,1]-1 - Optimized by BOBYQA

Example

{
"name": "root",
"type": "AnomalyDetector",
"params": {
"type": "HOLT_WINTERS",
"component.sensitivity": "4",
"component.monitoringGranularity": "P1D",
"component.lookbackPeriod": "P14D",
... # shared parameters
},
"inputs": [
{ # data with historical data for training
"targetProperty": "current",
"sourcePlanNode": "currentData",
"sourceProperty": "currentOutput"
}
]
}