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Hierarchical Spatio-Temporal Change-Point Detection

Author

Listed:
  • Mehdi Moradi
  • Ottmar Cronie
  • Unai Pérez-Goya
  • Jorge Mateu

Abstract

Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data.

Suggested Citation

  • Mehdi Moradi & Ottmar Cronie & Unai Pérez-Goya & Jorge Mateu, 2023. "Hierarchical Spatio-Temporal Change-Point Detection," The American Statistician, Taylor & Francis Journals, vol. 77(4), pages 390-400, October.
    • Handle: RePEc:taf:amstat:v:77:y:2023:i:4:p:390-400
    DOI: 10.1080/00031305.2023.2191670
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    Cited by:

    1. J. Derek Tucker & Drew Yarger, 2024. "Elastic functional changepoint detection of climate impacts from localized sources," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.

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