Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series

Abstract

We propose a new solution under the Bayesian framework to simultaneously estimate mean-based asynchronous changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM-based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The proposed linear model approach increases the robustness of our method to variability in the CUSUM process, which, combined with our spatial correlation model, improves changepoint estimation near the edges. We demonstrate through extensive simulation studies that our method outperforms existing functional changepoint estimators in terms of both estimation accuracy and uncertainty quantification, under either weak or strong spatial correlation, and weak or strong change signals. Finally, we demonstrate our method using a temperature data set and a coronavirus disease 2019 (COVID-19) study. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00519-w.

Elastic functional changepoint detection of climate impacts from localized sources

“…In addition, while we have treated each location individually, comprehensively studying the spatial or multivariate problem of how functional time series at different locations relate to each other would be a fruitful direction for future research that the authors are currently studying. Paynabar et al (2016), Wang et al (2022), and Moradi et al (2023) approach this problem in three different ways. Such an approach will also lend itself more to the multiple testing problem.…”