Selective review of offline change point detection methods

As of 2020, the best change point detection overview I found.

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.

Reference

Charles Truong, Laurent Oudre, Nicolas Vayatis “Selective review of offline change point detection methods” (2020) DOI: 10.1016/j.sigpro.2019.107299 arXiv:1801.00718

@Article{truong2020,
  title = {Selective review of offline change point detection methods},
  volume = {167},
  issn = {01651684},
  url = {http://arxiv.org/abs/1801.00718},
  doi = {10.1016/j.sigpro.2019.107299},
  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.},
  urldate = {2020-06-30},
  journal = {Signal Processing},
  author = {Truong, Charles and Oudre, Laurent and Vayatis, Nicolas},
  month = {feb},
  year = {2020},
  note = {arXiv: 1801.00718},
  arxiv = {1801.00718},
  keywords = {overview},
  pages = {107299}
}