tsseg.algorithms.bocd package

BOCD (Bayesian Online Change-point Detection) — posterior thresholding.

Description

This offline Bayesian change-point detector integrates out the mean and variance of each segment under a conjugate Normal-Gamma prior, constructs a run-length posterior via dynamic programming, and selects change points by thresholding the posterior probability of a boundary. It extends the classical framework of Fearnhead (2006) and Adams & MacKay (2007) to multivariate time series.

A constant hazard function \(H(\tau) = 1/\lambda\) controls the prior expectation of segment length. Two strategies handle multivariate inputs:

• "l2" — reduces channels via L2 norm before inference.

• "ensembling" — runs per-channel inference and aggregates change points.

Type: change point detection

Supervision: unsupervised or semi-supervised

Scope: univariate and multivariate

Parameters

Name	Type	Default	Description
hazard_lambda	float	300	Expected run length (constant hazard = 1/lambda).
mu	float	0.0	Prior mean of segment observations.
kappa	float	1.0	Strength of the prior mean (normal precision).
alpha	float	1.0	Shape of the inverse-gamma prior over variance.
beta	float	1.0	Scale of the inverse-gamma prior over variance.
truncate	int	-40	Log-probability truncation threshold for the DP matrix.
cp_prob_threshold	float	0.05	Minimum posterior probability to accept a change point.
min_distance	int	25	Minimum distance (samples) between successive change points.
max_cps	int / None	None	Optional upper bound on the number of change points.
multivariate_strategy	str	"l2"	"l2" (reduce via L2 norm) or "ensembling" (per-channel).
tolerance	float	0	Tolerance for aggregating change points in ensembling mode.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import BOCDDetector

detector = BOCDDetector(hazard_lambda=200, cp_prob_threshold=0.1)
labels = detector.fit_predict(X)

Implementation: Adapted from hildensia/bayesian_changepoint_detection. Apache License 2.0.

Reference: Fearnhead (2006), Statistics and Computing; Adams & MacKay (2007), arXiv.

Submodules

tsseg.algorithms.bocd.bayesian_models module

tsseg.algorithms.bocd.detector module

Module contents