Algorithms

Docs > Dashboards > Functions > 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:

Seasonality: By default, the robust and agile algorithms use weekly seasonality, which requires three weeks of historical data to compute the baseline.

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.