| 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.
| 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.
| 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.Consult the other available functions:
