tsseg.algorithms.eagglo package

E-Agglo — energy-based agglomerative change point detection.

Description

E-Agglo is a non-parametric, hierarchical agglomerative algorithm for detecting multiple change points in multivariate time series. Neighbouring segments are sequentially merged when the merge maximises a goodness-of-fit statistic based on energy distances. Unlike classical agglomerative clustering, this procedure preserves the temporal ordering.

A divergence parameter \(\alpha\in(0,2]\) controls the distance exponent. An optional penalty function can regularise against over-segmentation.

Type: change point detection

Supervision: fully unsupervised

Scope: univariate and multivariate

Complexity: \(O(n^{2})\)

Requires: numba

Parameters

Name	Type	Default	Description
member	array-like / None	None	Initial cluster membership. None = one cluster per point.
alpha	float	1.0	Divergence exponent in \((0, 2]\).
penalty	str / callable / None	None	Penalty function ("len_penalty", "mean_diff_penalty" or callable).

Usage

from tsseg.algorithms import EAggloDetector

detector = EAggloDetector(alpha=1.0)
labels = detector.fit_predict(X)

Implementation: Adapted from aeon. BSD 3-Clause.

Reference: Matteson & James (2014), A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data, JASA.

Submodules

tsseg.algorithms.eagglo.detector module

Module contents