Detecting Multiple Changepoints by Exploiting Their Spatiotemporal Correlations: A Bayesian Hierarchical Approach

Xian Chen (), Kun Huang (), Weichi Wu () and Hai Jiang ()
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Xian Chen: Department of Management Science and Engineering, Shanghai University, Shanghai 200444, China
Kun Huang: Department of Statistics, Texas A&M University, College Station, Texas 77840
Weichi Wu: Department of Statistics and Data Science, Tsinghua University, Beijing 100084, China
Hai Jiang: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China

INFORMS Joural on Data Science, 2025, vol. 4, issue 2, 133-153

Abstract: Capturing the nonstationarity of spatiotemporal data over time via changepoints has received increasing attention in various research fields. Although extensive studies have been conducted to investigate changepoint detection with spatiotemporal data, research on detecting multiple clusters of spatiotemporally correlated changepoints has remained unexplored. In this paper, we propose a multilayer Bayesian hierarchical model: The first layer uncovers the spatiotemporal correlations of changepoints based on multiple propagation binary variables, which describe the occurrences of change propagations. The second and third layers compose nonhomogeneous hidden Markov models to capture time series data and their state sequences, in which changes of states signify changepoints. We perform Bayesian inference for changepoints and change propagations via a forward-backward algorithm that combines recursion and Gibbs sampling. Based on the experiments with simulated data, we show that our method significantly improves the detection accuracy toward spatiotemporally correlated changepoints. A real-world application to bike-sharing data also demonstrates the effectiveness of our method. This research has significant relevance to companies operating systems across geographical regions, as it enables a more robust understanding of emerging trends and shifts in spatiotemporal data.

Keywords: multiple changepoint detection; spatiotemporal correlations; Bayesian hierarchical models; non-homogeneous hidden Markov models; forward-backward algorithms (search for similar items in EconPapers)
Date: 2025
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