Selective review of offline change point detection methods

TL;DR

This paper provides a structured survey of offline multivariate change point detection algorithms, categorizing them by cost function, search method, and change constraints, and offers implementations in a Python package.

Contribution

It introduces a comprehensive, structured review framework for change point detection methods and provides accessible implementations via the ruptures package.

Findings

Organized change point detection algorithms by key elements

Reviewed various algorithms and their methodological strategies

Provided Python implementations for practical use

Abstract

This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes. Each of those elements is described, reviewed and discussed separately. Implementations of the main algorithms described in this article are provided within a Python package called ruptures.