# Algorithms

## Anomalies

FunctionDescriptionExample
`anomalies()`Overlay a gray band on the metric showing the expected behavior of a series based on past.`anomalies(METRIC_NAME>{*}, '<ALGORITHM>', <BOUNDS>)`

The `anomalies()` function has two parameters:

• `ALGORITHM`: Methodology used to detect anomalies.
• `BOUNDS`: Width of the gray band. `bounds` can be interpreted as the standard deviations for your algorithm; a value of 2 or 3 should be large enough to include most “normal” points.

Note: If you are using the agile or robust anomaly detection algorithms with weekly or daily seasonality, you can update your anomaly detection monitor to account for a local timezone using both the API and the UI.

Here’s a two-minute video walkthrough:

## Outliers

FunctionDescriptionExample
`outliers()`Highlight outliers series.`outliers(<METRIC_NAME>{*}, '<ALGORITHM>', <TOLERANCE>, <PERCENTAGE>)`

The `outliers()` function has three parameters:

• `ALGORITHM`: The outliers algorithm to use.
• `TOLERANCE`: The tolerance of the outliers algorithm.
• `PERCENTAGE`: The percentage of outlying points required to mark a series as an outlier (available only for MAD and scaledMAD algorithms)

## Forecast

FunctionDescriptionExample
`forecast()`Predicts where a metric is heading in the future.`forecast(<METRIC_NAME>{*}, '<ALGORITHM>', <DEVIATIONS>)`

The `forecast()` function has two parameters:

• `ALGORITHM`: The forecasting algorithm to use - select `linear` or `seasonal`. For more information about these algorithms, see the Forecast Algorithms section.
• `DEVIATIONS`: The width of the range of forecasted values. A value of 1 or 2 should be large enough to forecast most “normal” points accurately.

A number of the graphing options disappear, as forecasts have a unique visualization. After successfully adding Forecast, your editor should show something like this: