Selective review of offline change point detection methods

Charles Truong, Laurent Oudre, Nicolas Vayatis
1/2/2018
cs.CEstat.COstat.ME

Official code: deepcharles/ruptures (2,100 stars, BSD-2-Clause, last commit 2026-10-06).

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.

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Code Implementations(1)

deepcharles/rupturesOfficial100%
BSD-2-Clause

ruptures: change point detection in Python

2,100203C, CythonJan 20, 20182 weeks agoBSD-2-Clause
change-point-detectionchangepointpythonsciencescientific-computing+1 more

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Cite this paper

@article{truong2018selective,
  title  = {Selective review of offline change point detection methods},
  author = {Charles Truong and Laurent Oudre and Nicolas Vayatis},
  year   = {2018},
  eprint = {1801.00718},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1801.00718}
}

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