Efficient Likelihood-Based Temporal Changepoint Detection in Spatio-Temporal Processes

The rapid advancements of scalable methodologies have opened new avenues for analyzing complex spatio-temporal data, which is crucial in understanding dynamic environmental phenomena. This paper introduces a likelihood-based methodology for detecting abrupt changes in time in spatio-temporal processes, a field where traditional time series methods fall short. Unlike recent approaches, we do not make the unrealistic assumption that data is independent across changepoints. Instead, we use a recently proposed family of covariance models that allows nonstationarity in time, and we propose a Markov approximation to reduce the computational burden of calculating likelihoods under this model. We apply our method to two years of daily wind speed data from various synoptic weather stations in Ireland, identifying a significant changepoint on July 24, 2021, which aligns with a major shift in weather patterns. This application not only demonstrates the method's utility in handling spatio-temporal datasets but also showcases its potential in broader environmental and climatic studies, offering a scalable solution for analyzing changing patterns in spatial data over time.