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Backward Degree a new index for online and offline change point detection based on complex network analysis

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  • Charakopoulos, Avraam
  • Karakasidis, Theodoros
Abstract
How to identify an upcoming transition in a time series continues to be an important open research issue. In various fields of physical sciences, engineering, finance and neuroscience abrupt changes can occur unexpectedly and are difficult to manage during the temporal evolution of the dynamic system. In this work, we developed a new unsupervised method called “Backward Degree” based on a new topological graph index that we introduce, which can be used to detect not only offline point of change, but also can effectively be used as an early warning system for online detection of upcoming abrupt changes. Specifically, based on the well-established algorithm “Visibility graph”, which was introduced by Lacasa et al. (2008) we convert a time series into a complex network and then we apply our proposed approach. The results, on a number of synthetic and financial datasets demonstrate that the proposed methodology correctly identifies change points during the evolution of time series validating the advantages of the proposed methodology for effective detection an upcoming abrupt transitions.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s037843712200591x
    DOI: 10.1016/j.physa.2022.127929
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    References listed on IDEAS

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    1. Yixiao Li & Gloria Lin & Thomas Lau & Ruochen Zeng, 2019. "A Review of Changepoint Detection Models," Papers 1908.07136, arXiv.org.
    2. Dietz, Simon & Rising, James & Stoerk, Thomas & Wagner, Gernot, 2021. "Economic impacts of tipping points in the climate system," LSE Research Online Documents on Economics 111807, London School of Economics and Political Science, LSE Library.
    3. Thies, Sven & Molnár, Peter, 2018. "Bayesian change point analysis of Bitcoin returns," Finance Research Letters, Elsevier, vol. 27(C), pages 223-227.
    4. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
    5. Marc Lavielle & Gilles Teyssière, 2007. "Adaptive Detection of Multiple Change-Points in Asset Price Volatility," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 129-156, Springer.
    6. Erdman, Chandra & Emerson, John W., 2007. "bcp: An R Package for Performing a Bayesian Analysis of Change Point Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i03).
    7. Simon Dietz & James Rising & Thomas Stoerk & Gernot Wagner, 2021. "Economic impacts of tipping points in the climate system," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(34), pages 2103081118-, August.
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