OR Aggregator¶

Aggregator that identifies a time step as anomalous if any of the components is flagged as anomalous (logical OR).

class darts.ad.aggregators.or_aggregator.OrAggregator[source]¶

Methods

`eval_accuracy`(actual_anomalies, series[, ...])	Aggregates the (sequence of) multivariate series given as input into one (sequence of) series and evaluates the results against true anomalies.
`predict`(series)	Aggregates the (sequence of) multivariate binary series given as input into a (sequence of) univariate binary series.

eval_accuracy(actual_anomalies, series, window=1, metric='recall')¶

Aggregates the (sequence of) multivariate series given as input into one (sequence of) series and evaluates the results against true anomalies.

Parameters

actual_anomalies (Sequence[TimeSeries]) – The (sequence of) ground truth of the anomalies (1 if it is an anomaly and 0 if not)
series (Sequence[TimeSeries]) – The (sequence of) multivariate binary series to aggregate
window (int) – (Sequence of) integer value indicating the number of past samples each point represents in the (sequence of) series. The parameter will be used by the function _window_adjustment_anomalies() in darts.ad.utils to transform actual_anomalies.
metric (str) – Metric function to use. Must be one of “recall”, “precision”, “f1”, and “accuracy”. Default: “recall”

Returns

(Sequence of) score for the (sequence of) series

Return type

Union[float, Sequence[float]]

predict(series)¶

Aggregates the (sequence of) multivariate binary series given as input into a (sequence of) univariate binary series.

Parameters: series (Union[TimeSeries, Sequence[TimeSeries]]) – The (sequence of) multivariate binary series to aggregate
Returns: (Sequence of) aggregated results
Return type: TimeSeries

Ensemble scikit-learn aggregator

Anomaly Models