tsseg.algorithms.time2state package

Time2State — unsupervised latent state inference.

Description

Time2State infers latent states in time series data using a Causal CNN-based encoder and a novel unsupervised loss function (LSE-Loss). A sliding window extracts subsequences; the encoder maps them to a low-dimensional latent space where a Dirichlet Process Gaussian Mixture Model (DPGMM) clusters the embeddings into states without requiring the number of states a priori.

The framework drastically reduces computational cost compared to operating on raw time series by compressing the representation before clustering.

Type: state detection

Supervision: unsupervised or semi-supervised

Scope: univariate and multivariate

Requires: PyTorch

Parameters

Name	Type	Default	Description
window_size	int	256	Sliding window size.
step	int	50	Step size of the sliding window.
n_states	int	20	Maximum number of states for DPGMM.
alpha	float	1e3	DPGMM concentration parameter.
batch_size	int	1	Training batch size.
nb_steps	int	20	Training optimisation steps.
lr	float	0.003	Learning rate.
depth	int	10	Depth of the Causal CNN.
out_channels	int	4	Encoder output channels.
reduced_size	int	80	CNN output dimension before the linear layer.
kernel_size	int	3	Convolution kernel size.
use_gpu	bool / None	None	Force GPU (None = auto-detect).
random_state	int / None	None	Random seed.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import Time2StateDetector

detector = Time2StateDetector(window_size=128, n_states=10)
states = detector.fit_predict(X)

Implementation: Adapted from original Time2State code.

Reference: Wang, Wu, Zhou & Cai (2023), Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data, SIGMOD.

Submodules

tsseg.algorithms.time2state.detector module

tsseg.algorithms.time2state.time2state module

Module contents