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Outlier detection

Outlier detection is the problem of identifying when one or more series within a group is behaving differently to the rest.

scenes-ml provides a SceneOutlierDetector component which will perform outlier detection and highlight any misbehaving series. It will also add a grey band indicating the 'cluster range' (the range of data that can be considered non-outlying), and (optionally) add annotations at time ranges where an outlier was detected.

Panel with outliers added

Usage​

The code example below demonstrates how to add outlier detection to a time series panel.

import { SceneOutlierDetector } from '@grafana/scenes-ml';

// Default values are shown here, all are optional.
const outlierDetector = new SceneOutlierDetector({
  sensitivity: 0.5,
  addAnnotations: false,
  pinned: false,
  onOutlierDetected: (outlier: Outlier) => {},
});
const panel = PanelBuilders.timeseries()
  .setHeaderActions([outlierDetector])
  .build();
note

Make sure you only add outlier detection to time series panels, as it rarely makes sense for other panel types.

Pinning results​

By default, baselines are recalculated on every state change, i.e. whenever the time range, query or interval changes. This isn't always desirable: for example, the user may want to zoom out and view the current forecasts in a future time range.

Enabling the pinned setting will freeze the current results, so they won't be recalculated as the time range or other settings are changed.

Technical details​

scenes-ml currently uses a variant of the DBSCAN algorithm to detect outliers. Additional algorithms may be added in future.