tsseg.algorithms.fluss package

FLUSS — Fast Low-cost Unipotent Semantic Segmentation.

Description

FLUSS is a domain-agnostic method for semantic segmentation of time series. It leverages the matrix profile (computed by stumpy) and derives the arc curve: for each time index the number of nearest-neighbour arcs crossing that index is counted. Valleys in the arc curve indicate semantic boundaries because few matching subsequences span across a true regime change.

A sequential peak search on the inverted arc curve yields the requested number of change points. FLUSS has only one parameter — the window size — which corresponds to the dominant period length.

Type: change point detection

Supervision: semi-supervised or unsupervised

Scope: univariate (multivariate via ensembling)

Requires: stumpy

Parameters

Name	Type	Default	Description
window_size	int	10	Sliding window size (use the dominant period length).
n_segments	int	2	Number of segments (= change points + 1).
exclusion_factor	int	5	Multiplying factor for the exclusion zone around detected points.
multivariate_strategy	str	"ensembling"	Strategy for multivariate inputs ("ensembling").
tolerance	float	0.01	Tolerance for aggregating change points.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import FLUSSDetector

detector = FLUSSDetector(window_size=50, n_segments=4)
labels = detector.fit_predict(X)

Implementation: Wrapper around stumpy. BSD 3-Clause.

Reference: Gharghabi, Yeh, Ding, Ding, Hibbing, LaMunion, Kaplan, Crouter & Keogh (2017, 2019), Domain Agnostic Online Semantic Segmentation, ICDM / DMKD.

Submodules

tsseg.algorithms.fluss.detector module

Module contents