Hierarchical spatio-temporal change-point detection
Journal article, 2023

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.

Point patterns

Satellite images

Functional data

Multivariate analysis


Land surface temperature



Mehdi Moradi

Umeå University

Ottmar Cronie

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Unai Pérez-Goya

University of Navarra

Jorge Mateu

Universidad Jaume I

American Statistician

0003-1305 (ISSN) 1537-2731 (eISSN)

Vol. In press

Subject Categories

Probability Theory and Statistics



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Latest update

5/3/2023 7