tsseg.algorithms.icid package

iCID — Isolation Distributional Kernel Change Interval Detection.

Description

iCID detects change intervals by projecting the time series into a high-dimensional distributional feature space (aNNEspace) and measuring cosine-distance dissimilarity between consecutive sliding windows. A z-score-based threshold \(\alpha\) determines whether a dissimilarity peak constitutes a true change.

The method is non-parametric, does not assume Gaussianity, and automatically selects the granularity through a list of psi (sub-sample) values.

Type: change point detection

Supervision: fully unsupervised

Scope: univariate and multivariate

Parameters

Name	Type	Default	Description
window_size	int	50	Sliding window size for computing dissimilarity.
alpha	float	0.5	Sensitivity factor (higher = less sensitive).
t	int	200	Iterations for aNNEspace transformation.
psi_list	list[int] / None	[2,4,8,16,32,64]	Sub-sample sizes controlling granularity.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import ICIDDetector

detector = ICIDDetector(window_size=100, alpha=0.3)
labels = detector.fit_predict(X)

Implementation: Origin: new code.

Reference: Cao, Ting, Liu, Cek & Angelova (2024), A new framework for change interval detection, JAIR.

Submodules

tsseg.algorithms.icid.detector module

Module contents