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.

中文翻译：

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离线变化点检测方法的选择性审查

本文对用于离线检测多元时间序列中的多个变化点的算法进行了选择性调查。采用通用但结构化的方法论策略来组织这一庞大的工作。更准确地说，本次审查中考虑的检测算法具有三个要素：成本函数、搜索方法和对更改次数的约束。这些要素中的每一个都分别进行了描述、审查和讨论。本文中描述的主要算法的实现是在一个名为 bursts 的 Python 包中提供的。