|Overlay a gray band on the metric showing the expected behavior of a series based on past.|
anomalies() function has two parameters:
ALGORITHM: Methodology used to detect anomalies.
BOUNDS: Width of the gray band.
boundscan 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.
|Highlight outliers series.|
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.
|Predicts where a metric is heading in the future.|
forecast() function has two parameters:
ALGORITHM: The forecasting algorithm to use - select
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:
Consult the other available functions: