tsseg.algorithms.igts package
IGTS — Information Gain based Temporal Segmentation.
Description
IGTS is a top-down greedy algorithm that locates the change point maximising the information gain at each step, then repeats on the resulting sub-signals. It works best on multivariate series where distribution shifts across channels provide discriminative evidence.
Warning
IGTS does not perform well on univariate series without augmentation.
Type: change point detection
Supervision: unsupervised or semi-supervised
Scope: multivariate (primarily)
Parameters
Name |
Type |
Default |
Description |
|---|---|---|---|
|
int |
|
Maximum number of change points. |
|
int |
|
Stride for candidate locations. |
Usage
from tsseg.algorithms import InformationGainDetector
detector = InformationGainDetector(k_max=8, step=5)
labels = detector.fit_predict(X)
Implementation: Adapted from aeon. BSD 3-Clause.
Reference: Sadri, Ren & Salim (2017), Information Gain-based Metric for Recognizing Transitions in Human Activities, Pervasive and Mobile Computing.