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Title: Modelling and detecting changes in spatial time series Authors:  Idris Eckley - Lancaster University (United Kingdom)
Paul Fearnhead - Lancaster University (United Kingdom)
Gaurav Agarwal - Lancaster University (United Kingdom) [presenting]
Abstract: Changepoints have been extensively studied for time series data, but there is limited literature on detecting changes in stochastic processes over time. A likelihood-based methodology is developed for the simultaneous estimation of both changepoints and model parameters of spatio-temporal processes. Contrasting to existing spatial changepoint methods, which fit a piecewise stationary model assuming independence across segments, we fit a nonstationary model without any independence assumption. To deal with the complexity of the full likelihood model, we propose a computationally efficient Markov approximation. We study the effect of such an approximation through a comprehensive set of simulations. Furthermore, we present a comparison with existing methodologies, both in the case of dependence and independence across segments. The method is employed for changepoint detection and missing data prediction in daily soil moisture concentrations across different sites in the United Kingdom over a period of two years.