MESST

MESST applies the entangled ESST score to multivariate block-Hankel matrices. It is intended for structural changes involving several channels.

Use the tuning guide for a parameter-by-parameter workflow and runtime advice.

Parameters

window_length : int

Subsequence length in samples.

n_windows : int, optional

Number of subsequences per side. Defaults to window_length // 2.

lag : int, optional

Separation between compared matrices. Defaults to n_windows.

rank : int, default 5

Number of joint singular directions used by the score.

scale : bool, default True

Min-max scale every channel independently to [1, 2].

method : {"rsvd"}, default "rsvd"

Decomposition method. Only randomized SVD is currently available.

random_rank : int, optional

Sampled randomized-SVD dimension. Defaults to at most rank + 10.

scoring_step : int, default 1

Distance between evaluated positions.

use_fast_hankel : bool, default False

Use the implicit fast block-Hankel representation.

Methods

transform(time_series)

time_series : numpy.ndarray, shape (n_samples, n_channels)

Multivariate signal with samples on axis 0.

Returns

score : numpy.ndarray, shape (n_samples,)

Joint entangled subspace-change score.

Minimal Example

import numpy as np
from changepoynt.algorithms.messt import MESST

t = np.linspace(0, 12 * np.pi, 400)
signal = np.column_stack((np.sin(t), np.cos(t)))
signal[200:, 1] = np.cos(2 * t[200:])
score = MESST(window_length=40).transform(signal)

Reference: Boelter et al. (2025).