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Bayesian On-line Changepoint Detection is extended to on-line model selectionand non-stationary spatio-temporal processes. We propose spatially structuredVector Autoregressions (VARs) for modelling the process between changepoints(CPs) and give an upper bound on the approximation error of such models. Theresulting algorithm performs prediction, model selection and CP detectionon-line. Its time complexity is linear and its space complexity constant, andthus it is two orders of magnitudes faster than its closest competitor. Inaddition, it outperforms the state of the art for multivariate data.

Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection