HOLT_WINTERS
Enterprise only
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
| name | description | default value |
|---|---|---|
component.sensitivity | Detection sensitivity scale from 0 - 10, mapping z-score from 1 to 3. | 5 |
component.lookbackPeriod | Historical 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.lookback | Deprecated. Prefer component.lookbackPeriod. Number of data points to use for training. | 60 |
component.pattern | Detect as an anomaly if the metric drop, rise or both directions. UP, DOWN, UP_OR_DOWN. | UP_OR_DOWN |
component.period | Deprecated. Prefer component.seasonalityPeriod. Seasonality biggest period in number of data points. | 7 |
component.seasonalityPeriod | Seasonality 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.alpha | Level smoothing factor. In [0,1] | -1 - Optimized by BOBYQA |
component.beta | Trend smoothing factor. In [0,1] | -1 - Optimized by BOBYQA |
component.gamma | Seasonal smoothing factor. In [0,1] | -1 - Optimized by BOBYQA |
Example
Recommended defaults
{
"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"
}
]
}