@article{truong_selective_2020,
	title = {Selective review of offline change point detection methods},
	volume = {167},
	issn = {0165-1684},
	url = {http://www.sciencedirect.com/science/article/pii/S0165168419303494},
	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.},
	language = {en},
	urldate = {2020-10-01},
	journal = {Signal Processing},
	author = {Truong, Charles and Oudre, Laurent and Vayatis, Nicolas},
	month = feb,
	year = {2020},
	keywords = {Change point detection, Segmentation, Statistical signal processing},
	pages = {107299},
}

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