Selective review of offline change point detection methods

By Charles Truong, Laurent Oudre, Nicolas Vayatis. Signal Processing, 2020

1,194 citations recorded by the source corpus.

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.

Subfields: Finance, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/05aeaa675d36a1ead5e53bbc209b6be8bc9e31ad

Citations in the corpus

• Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus (2026, unknown): “We use PELT implementation included in the ruptures package for Python [63, 64].”