Algorithms

Anomalies

Function	Description	Example
`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:

See the Anomaly Monitor page for more info.

Outliers

Function	Description	Example
`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)

See the Outlier Monitor page for more info.

Forecast

Function	Description	Example
`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: