Anomaly Detectors

Detectors provide binary anomaly classification on time series. They can typically be used to transform anomaly scores time series into binary anomaly time series.

Some detectors are trainable. For instance, QuantileDetector emits a binary anomaly for every time step where the observed value(s) are beyond the quantile(s) observed on the training series.

The main functions are fit() (for the trainable detectors), detect() and eval_accuracy().

fit() trains the detector over the history of one or multiple time series. It can for instance be called on series containing anomaly scores (or even raw values) during normal times. The function detect() takes an anomaly score time series as input, and applies the detector to obtain binary predictions. The function eval_accuracy() returns the accuracy metric (“accuracy”, “precision”, “recall” or “f1”) between a binary prediction time series and some known binary ground truth time series indicating the presence of anomalies.