MEAN_VARIANCE
Detects an anomaly if the metric is not in mean ± n*std. mean and std (standard deviation) are estimated with historical data. The amount of historical data to use is set via lookbackPeriod.
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. 5 means n=1 sigma. The smaller, the less anomalies are detected. | 5 |
component.lookbackPeriod | Historical period to use to estimate mean and std. 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 to estimate mean and std. | 52 |
component.seasonalityPeriod | Seasonality to consider when computing mean and variance. Possible values are P7D (weekly and smaller periods), P1D (daily and smaller periods), PT0S (no seasonality management). Eg: with P7D, a Monday 12 AM value will be estimated from mean and variance of the previous Monday 12 AM values. Requires component.monitoringGranularity, see shared parameters. | PTOS |
component.pattern | Detect as an anomaly if the metric drop, rise or both directions. UP, DOWN, UP_OR_DOWN. | UP_OR_DOWN |
Example
{
"name": "root",
"type": "AnomalyDetector",
"params": {
"type": "MEAN_VARIANCE",
"component.monitoringGranularity": "P1D",
"component.lookbackPeriod": "P14D",
"component.sensitivity": "5",
"component.pattern": "UP",
... # shared parameters
},
"inputs": [
{ # data with historical data for mean/std estimation
"targetProperty": "current",
"sourcePlanNode": "currentDataFetcher",
"sourceProperty": "currentOutput"
}
]
}