tsseg.algorithms.ggs package

GGS — Greedy Gaussian Segmentation.

Description

GGS segments a multivariate time series by modelling each segment as i.i.d. samples from a multivariate Gaussian distribution. The algorithm maximises the total log-likelihood minus a covariance-regularisation term \(\lambda\). A greedy dynamic-programming search finds an approximate solution in linear time.

The maximum number of change points k_max is an upper bound; the algorithm may return fewer if additional splits do not improve the regularised likelihood.

Type: state detection
Supervision: unsupervised or semi-supervised
Scope: univariate and multivariate

Parameters

Name	Type	Default	Description
`k_max`	int	`10`	Maximum number of change points.
`lamb`	float	`1.0`	Regularisation \(\lambda\) (>= 0). Controls covariance shrinkage.
`max_shuffles`	int	`250`	Maximum number of shuffles during the greedy search.
`verbose`	bool	`False`	Print progress information.
`random_state`	int / None	`None`	Random seed.

Usage

from tsseg.algorithms import GreedyGaussianDetector

detector = GreedyGaussianDetector(k_max=8, lamb=1.0)
labels = detector.fit_predict(X)

Implementation: Adapted from aeon. BSD 3-Clause.

Reference: Hallac, Nystrup & Boyd (2019), Greedy Gaussian Segmentation of Multivariate Time Series, Advances in Data Analysis and Classification.

Submodules

tsseg.algorithms.ggs.detector module

Module contents