MEAN_VARIANCE - StarTree Docs

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"
    }
  ]
}

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