> ## Documentation Index
> Fetch the complete documentation index at: https://docs.startree.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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](./#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](./#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

```json theme={null}
{
  "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"
    }
  ]
}
```