Detector

CircularBinarySegmentation

Circular binary segmentation for multiple segment anomaly detection.

Binary segmentation type changepoint detection algorithms recursively split the data into two segments, and test whether the two segments are different. Circular binary segmentation [1] is a variant of binary segmentation where the statistical test (anomaly score) is applied to compare the data behaviour of an inner interval subset with the surrounding data contained in an outer interval. In other words, the null hypothesis within each outer interval is that the data is stationary, while the alternative hypothesis is that there is a segment anomaly within the outer interval.

Schnellstart

python

from sktime.detection.circular_binseg import CircularBinarySegmentation

estimator = CircularBinarySegmentation(anomaly_score=None, penalty=None, min_segment_length=5, max_interval_length=1000, growth_factor=1.5)

Parameter(5)

anomaly_scoreBaseIntervalScorer, optional, default=L2Cost(): The local anomaly score to use for anomaly detection. If a cost is given, it is converted to a local anomaly score using the LocalAnomalyScore class.
penaltynp.ndarray or float, optional, default=None: The penalty to use for anomaly detection. If the anomaly score is penalised (anomaly_score.get_tag("is_penalised")) the penalty will be ignored. The different types of penalties are:
float: A constant penalty applied to the sum of scores across all variables in the data.
np.ndarray: A penalty array of the same length as the number of columns in the data, where element i of the array is the penalty for i+1 variables being affected by an anomaly. The penalty array must be positive and increasing (not strictly). A penalised score with a linear penalty array is faster to evaluate than a nonlinear penalty array.
None: A default penalty is created in predict based on the fitted score using the make_bic_penalty function.
min_segment_lengthint, default=5: Minimum length between two changepoints. Must be greater than or equal to 1.
max_interval_lengthint, default=1000: The maximum length of an interval to estimate a changepoint in. Must be greater than or equal to 2 * min_segment_length.
growth_factorfloat, default=1.5: The growth factor for the seeded intervals. Intervals grow in size according to interval_len = max(interval_len + 1, floor(growth_factor * interval_len)), starting at interval_len = min_interval_length. It also governs the amount of overlap between intervals of the same length, as the start of each interval is shifted by a factor of 1 + 1 / growth_factor. Must be a float in (1, 2].

Beispiele

>>> from sktime.detection.circular_binseg import CircularBinarySegmentation
>>> import numpy as np, pandas as pd
>>> rng = np. random. default_rng (42)
>>> X = pd. DataFrame (rng. standard_normal ((75, 1)))
>>> X. iloc [20: 30 ] += 10.0
>>> X. iloc [50: 55 ] += 20.0
>>> detector = CircularBinarySegmentation (penalty = 20.0)
>>> detector. fit_predict (X)

Referenzen

[1]

Olshen, A. B., Venkatraman, E. S., Lucito, R., & Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5(4), 557-572.