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