tsseg.algorithms.binseg package

Binary Segmentation (BinSeg) — fast sequential change point detection.

Description

Binary segmentation is a greedy, top-down procedure: it first detects a single change point in the full signal, then splits the series at that point and repeats the operation on each sub-signal until a stopping criterion is met.

The benefits include low complexity — \(O(C\,n\log n)\) where n is the number of samples and C the cost of evaluating the cost function on one sub-signal — and the ability to work whether the number of regimes is known beforehand or not.

Type: change point detection

Supervision: unsupervised or semi-supervised

Scope: univariate and multivariate

Complexity: \(O(C\,n\log n)\)

Parameters

Name	Type	Default	Description
n_cps	int / None	None	Number of change points. When None, uses penalty or epsilon.
model	str	"l2"	Ruptures cost model ("l2", "l1", "rbf", "linear", "normal", "ar").
min_size	int	2	Minimum segment length.
jump	int	5	Sub-sampling factor for candidate breakpoints.
penalty	float / None	10	Penalty value (BIC-style). Mutually exclusive with n_cps / epsilon.
epsilon	float / None	None	Reconstruction-error tolerance. Mutually exclusive with n_cps / penalty.
custom_cost	BaseCost / None	None	Pre-instantiated ruptures cost object.
cost_params	dict / None	None	Additional keyword arguments forwarded to the cost factory.
axis	int	0	Axis representing time.

Usage

from tsseg.algorithms import BinSegDetector

detector = BinSegDetector(model="l2", n_cps=3)
labels = detector.fit_predict(X)

# Unknown number of change points — use a penalty
detector = BinSegDetector(model="l2", penalty=10)
labels = detector.fit_predict(X)

Implementation: Vendored from ruptures v1.1.8. BSD 2-Clause.

Reference: Bai (1997), Econometric Theory; Fryzlewicz (2014), The Annals of Statistics.

Submodules

tsseg.algorithms.binseg.detector module

Module contents