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arXiv · 1801.00718

Selective review of offline change point detection methods

Charles Truong·Laurent Oudre·Nicolas Vayatis
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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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Charles Truong, Laurent Oudre, Nicolas Vayatis. 2018-01-02. Selective review of offline change point detection methods. https://doi.org/10.1016/j.sigpro.2019.107299

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