tsseg.algorithms.tglad package

tGLAD — temporal Graph-difference change point detection.

Description

tGLAD segments multivariate time series by tracking the evolution of conditional independence (CI) graphs over time. A sliding window converts the series into overlapping intervals; the uGLAD sparse graph recovery model (multitask mode) recovers a precision matrix (CI graph) for each interval simultaneously. A second-order trajectory-tracking algorithm measures graph-difference scores, and an allocation algorithm produces the final segmentation.

Type: change point detection

Supervision: fully unsupervised

Scope: univariate and multivariate

Requires: PyTorch, networkx

Parameters

Name	Type	Default	Description
window_size	int	512	Time steps per uGLAD window.
stride	int	128	Step between successive windows.
batch_size	int	8	Windows processed together by the multitask solver.
threshold	float	0.5	Min Frobenius distance to trigger a change point.
min_spacing	int / None	None	Min distance between emitted CPs (default: stride).
epochs	int	2000	uGLAD training epochs per batch.
learning_rate	float	0.001	Optimiser learning rate.
glad_iterations	int	5	Unrolled GLAD iterations.
eval_offset	float	0.1	Eigenvalue regularisation for batch covariance.
verbose	bool	False	Print progress information.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import TGLADDetector

detector = TGLADDetector(window_size=256, stride=64, threshold=0.4)
labels = detector.fit_predict(X)

Implementation: Origin: new code. Inspired by the tGLAD paper.

Reference: Imani & Shrivastava (2023), Are uGLAD? Time will tell!

Submodules

tsseg.algorithms.tglad.detector module

Module contents