# outlier Metrics Operator

The metrics `outlier`

operator identifies metrics data points that are outside the range of expected values. Outliers help you spot unusual behavior in your metrics visualizations and track the behavior over time.

`outlier`

tracks the moving average and standard deviation of a time series over a specified time window, and calculates a threshold band, outside of which data points are considered outliers. You can use optional qualifiers to specify the time window, the number of standard deviations beyond which a data point is considered an outlier, and the directionality of the deviation.

You can't directly reference the `outlier`

operator in a metrics monitor, however, you can use the outlier detection method in a metrics monitor to alert based on outlier events.

## Syntax

`outlier [window=<time_unit_or_number>, threshold=<number>, direction=[ +- | + | - ]]`

Where:

`window`

is the range over which to calculate the moving average and standard deviation of the time series.`window`

can be specified with time units (s, m, h), or it can be specified without time units. Default: 5m.

If you use `outlier`

in the Classic Metrics UI, if you specify the `window`

parameter without supplying a unit of time, the window duration applied will be in the units used in the quantization of the query.

`threshold`

is the number of standard deviations from the moving average that defines the threshold band. Default: 3`direction`

specifies what deviation direction should trigger violations: positive deviations (+), negative deviations (-), or both (+-). Default: +-

In the visualization, the threshold band is the part shaded in pink. The outlier values are represented by the pink triangles.