tsseg.algorithms.tscp2 package

TS-CP² — change point detection with contrastive predictive coding.

Description

TS-CP² learns temporal representations using a Temporal Convolutional Network (TCN) trained with a contrastive predictive coding (CPC) objective. Pairs of consecutive windows are encoded; if their embeddings are similar the boundary between them is unlikely to be a change point, while dissimilar embeddings signal a regime change.

The contrastive loss (configurable: NCE, debiased CL, focal CL, hard-negative CL) encourages the encoder to capture the distributional structure of each segment. A similarity curve is built from all adjacent-window pairs and thresholded (or top-K peaks selected) to produce change points.

Type: change point detection

Supervision: unsupervised or semi-supervised

Scope: univariate and multivariate

Requires: TensorFlow and tcn

Parameters

Name	Type	Default	Description
window_size	int	128	Sliding window size for building window pairs.
n_cps	int / None	None	Number of change points (None = auto-threshold).
similarity_threshold	float / None	None	Custom similarity threshold for detecting CPs.
stride	int	5	Step between successive window pairs.
code_size	int	32	Dimension of the learned representation.
nb_filters	int	64	Number of TCN filters.
kernel_size	int	4	TCN kernel size.
dilations	tuple	(1,2,4,8)	Dilation factors for TCN layers.
nb_stacks	int	2	Number of TCN stacks.
dropout_rate	float	0.0	Dropout rate.
batch_size	int	64	Training batch size.
epochs	int	100	Training epochs.
learning_rate	float	1e-3	Optimiser learning rate.
loss	str	"nce"	Contrastive loss ("nce", "dcl", "fc", "harddcl").
temperature	float	0.1	Temperature for contrastive logits.
similarity	str	"cosine"	Similarity function.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import TSCP2Detector

detector = TSCP2Detector(window_size=128, n_cps=5, epochs=50)
labels = detector.fit_predict(X)

Implementation: TensorFlow reimplementation adapted from original code by Deldari et al.

Reference: Deldari, Smith, Xue & Salim (2021), Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding, WWW.

Submodules

tsseg.algorithms.tscp2.detector module

tsseg.algorithms.tscp2.losses module

tsseg.algorithms.tscp2.network module

Module contents