tsseg.algorithms.espresso package

ESPRESSO — Entropy and Shape-aware Time Series Segmentation.

Description

ESPRESSO exploits both entropy and temporal shape properties to segment multi-dimensional time series. The method builds a semantic density matrix from the matrix profile using an arc-curve expansion procedure:

Compute the matrix profile with stumpy.
Iteratively expand arc sets (controlled by chain_len) to accumulate segment-boundary evidence.
Detect peaks in the resulting density curve; peaks signal change points.

ESPRESSO differs from methods that focus exclusively on statistical or temporal properties.

Type: change point detection
Supervision: semi-supervised (n_segments required)
Scope: univariate and multivariate

Parameters

Name	Type	Default	Description
`window_size`	int	`64`	Subsequence length for the matrix profile (>= 4).
`chain_len`	int	`3`	Iterations for expanding arc sets.
`n_segments`	int / None	`None`	Target number of segments (>= 2 at predict time).
`peak_distance_fraction`	float	`0.01`	Minimum spacing between peaks as a fraction of the series length.
`axis`	int	`0`	Time axis.
`random_state`	int / None	`None`	Seed for the internal RNG.

Usage

from tsseg.algorithms import EspressoDetector

detector = EspressoDetector(window_size=64, chain_len=5, n_segments=4)
labels = detector.fit_predict(X)

Implementation: Reimplemented from the original ESPRESSO paper. Origin: new code.

Reference: Deldari, Smith, Sadri & Salim (2020), ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn, UbiComp.

Submodules

tsseg.algorithms.espresso.detector module

Module contents