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()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()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.

Other functions