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A Review of Changepoint Detection Models

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  • Yixiao Li
  • Gloria Lin
  • Thomas Lau
  • Ruochen Zeng

Abstract

The objective of the change-point detection is to discover the abrupt property changes lying behind the time-series data. In this paper, we firstly summarize the definition and in-depth implication of the changepoint detection. The next stage is to elaborate traditional and some alternative model-based changepoint detection algorithms. Finally, we try to go a bit further in the theory and look into future research directions.

Suggested Citation

  • Yixiao Li & Gloria Lin & Thomas Lau & Ruochen Zeng, 2019. "A Review of Changepoint Detection Models," Papers 1908.07136, arXiv.org.
    • Handle: RePEc:arx:papers:1908.07136
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    File URL: http://arxiv.org/pdf/1908.07136
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    References listed on IDEAS

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    1. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.
    2. Paul Fearnhead & Zhen Liu, 2007. "On‐line inference for multiple changepoint problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 589-605, September.
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    Cited by:

    1. Thangjam, Aditya & Jaipuria, Sanjita & Dadabada, Pradeep Kumar, 2023. "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks," Applied Energy, Elsevier, vol. 333(C).
    2. Charakopoulos, Avraam & Karakasidis, Theodoros, 2022. "Backward Degree a new index for online and offline change point detection based on complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.

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