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¶

Quantile Detector