Skip to main content

MEAN_VARIANCE

Detect an anomaly if the metric is not in mean ± n*std.
mean and std (standard deviation) are estimated with historical data. n can be controlled.

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. 5 means n=1 sigma.5
component.lookbackPeriodHistorical 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.lookbackDeprecated. Prefer component.lookbackPeriod. Number of data points to use to estimate mean and std.52
component.seasonalityPeriodSeasonality 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.patternDetect 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"
}
]
}