Anomaly Models¶
Anomaly models make it possible to use any of Darts’ forecasting or filtering models to detect anomalies in time series.
The basic idea is to compare the predictions produced by a fitted model (the forecasts or the filtered series) with the actual observations, and to emit an anomaly score describing how “different” the observations are from the predictions.
An anomaly model takes as parameters a model and one or multiple scorer objects.
The key method is score(), which takes as input one (or multiple)
time series and produces one or multiple anomaly scores time series, for each provided series.
ForecastingAnomalyModel works with Darts forecasting models, and FilteringAnomalyModel
works with Darts filtering models.
The anomaly models can also be fitted by calling fit(), which trains the scorer(s)
(in case some are trainable), and potentially the model as well.
The function eval_accuracy() is the same as score(), but outputs the score of an agnostic
threshold metric (“AUC-ROC” or “AUC-PR”), between the predicted anomaly score time series, and some known binary
ground-truth time series indicating the presence of actual anomalies.
Finally, the function show_anomalies() can also be used to visualize the predictions
(in-sample predictions and anomaly scores) of the anomaly model.