Selective review of offline change point detection methods
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jan 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 4.
8 papers.
- 2021Deep Time Series Forecasting with Shape and Temporal Criteriacited by 10
- 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inferencecited by 10
- 2021InDiD: Instant Disorder Detection via Representation Learningcited by 4
- 2023Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structurescited by 2
- 2023A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanismcited by 2
- 2022Unsupervised Change Detection using DRE-CUSUMcited by 1
- 2023Self-Supervised Transformer Architecture for Change Detection in Radio Access Networkscited by 1
- 2023Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimizationcited by 0
- 2023Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization6 Dec 2023
- 2023A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanism7 Sep 2023
- 2023Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures27 Jun 2023
- 2023Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks3 Feb 2023
- 2022Unsupervised Change Detection using DRE-CUSUM27 Jan 2022
- 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference18 Oct 2021
- 2021InDiD: Instant Disorder Detection via Representation Learning4 Jun 2021
- 2021Deep Time Series Forecasting with Shape and Temporal Criteria9 Apr 2021