Interquartile Range (IQR) Detector#

Flags anomalies that are beyond the IQR (between the third and the first quartile) of historical data by some factor of it’s difference (typically 1.5). This is similar to a threshold-based detector, but the thresholds are computed as distances from the IQR of historical data when the detector is fitted.

class darts.ad.detectors.iqr_detector.IQRDetector(scale=1.5)[source]#

Bases: QuantileDetector

IQR Detector

Flags values that lie outside of the interquartile range (IQR) by more than a certain factor of IQR’s value as anomalies. The factor is passed in the scale parameter.

If a single value is provided for scale, this same value will be used across all components of the series.

If a sequences of values is given for the scale parameter, it’s length must match the dimensionality of the series passed.

Parameters:

scale (Union[Sequence[float], float]) – (Sequence of) scale(s) used to indicate what distance from the IQR constitutes an anomaly. Defaults to 1.5. Must be non-negative. If a sequence, must match the dimensionality of the series this detector is applied to.

Attributes

high_threshold

low_threshold

Methods

detect(series[, name])

Detect anomalies on given time series.

eval_metric(anomalies, pred_scores[, ...])

Score the results against true anomalies.

fit(series)

Trains the detector on the given time series.

fit_detect(series)

Trains the detector and detects anomalies on the same series.
detect(series, name='series')#

Detect anomalies on given time series.

Parameters:

  • series (Union[TimeSeries, Sequence[TimeSeries]]) – The (sequence of) series on which to detect anomalies.

  • name (str) – The name of series.

Returns:

binary prediction (1 if considered as an anomaly, 0 if not)

Return type:

Union[TimeSeries, Sequence[TimeSeries]]

eval_metric(anomalies, pred_scores, window=1, metric='recall')#

Score the results against true anomalies.

Parameters:

  • anomalies (Union[TimeSeries, Sequence[TimeSeries]]) – The (sequence of) ground truth binary anomaly series (1 if it is an anomaly and 0 if not).

  • pred_scores (Union[TimeSeries, Sequence[TimeSeries]]) – The (sequence of) of estimated anomaly score series indicating how anomalous each window of size w is.

  • window (int) – Integer value indicating the number of past samples each point represents in the pred_scores.

  • metric (Literal['recall', 'precision', 'f1', 'accuracy']) – The name of the metric function to use. Must be one of “recall”, “precision”, “f1”, and “accuracy”. Default: “recall”.

Returns:

Metric results for each anomaly score

Return type:

Union[float, Sequence[float], Sequence[Sequence[float]]]

fit(series)#

Trains the detector on the given time series.

Parameters:

series (Union[TimeSeries, Sequence[TimeSeries]]) – Time (sequence of) series to be used to train the detector.

Returns:

Fitted Detector.

Return type:

self

fit_detect(series)#

Trains the detector and detects anomalies on the same series.

Parameters:

series (Union[TimeSeries, Sequence[TimeSeries]]) – Time series to be used for training and be detected for anomalies.

Returns:

Binary prediction (1 if considered as an anomaly, 0 if not)

Return type:

Union[TimeSeries, Sequence[TimeSeries]]

property high_threshold#
property low_threshold#