tsseg.algorithms.e2usd package

E2USD — Efficient-yet-Effective Unsupervised State Detection.

Description

E2USD combines two key ideas for scalable, accurate unsupervised state detection in multivariate time series:

1. DDEM (Decomposed Dual-view Embedding Module) — a lightweight encoder that compresses sliding windows into low-dimensional representations using FFT-based compression and a decomposed contrastive learning objective.

2. DPGMM clustering — a Dirichlet Process Gaussian Mixture Model that automatically determines the number of states from the learned embeddings.

A False Negative Cancellation Contrastive Learning method (FNCCLearning) is used to counteract false negatives and produce cluster-friendly embedding spaces.

Type: state detection

Supervision: unsupervised or semi-supervised

Scope: multivariate

Requires: PyTorch

Parameters

Name	Type	Default	Description
window_size	int	256	Size of the sliding window.
step	int	50	Step size of the sliding window.
n_states	int	20	Maximum number of states for DPGMM clustering.
alpha	float	1e3	Concentration parameter for DPGMM.
batch_size	int	1	Training batch size.
nb_steps	int	20	Number of optimisation steps.
lr	float	0.003	Learning rate.
depth	int	1	Depth of the DDEM encoder network.
out_channels	int	4	Number of output channels of the encoder.
reduced_size	int	80	Dimension of the CNN output before the final linear layer.
kernel_size	int	3	Kernel size for CNN convolutions.
use_gpu	bool / None	None	Force GPU usage (None = auto-detect).
random_state	int / None	None	Random seed.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import E2USDDetector

detector = E2USDDetector(window_size=256, n_states=10, nb_steps=20)
states = detector.fit_predict(X)

Implementation: Adapted from AI4CTS/E2Usd. No licence found in original repository.

Reference: Lai, Zhao, Li, Qian, Zhang & Jensen (2024), E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series, WWW.

Submodules

tsseg.algorithms.e2usd.detector module

tsseg.algorithms.e2usd.e2usd module

Module contents