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Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Jeremias Knoblauch · Theodoros Damoulas
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.
Author Information
Jeremias Knoblauch (Warwick University)
Theodoros Damoulas (University of Warwick)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Poster: Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection »
Thu. Jul 12th 04:15 -- 07:00 PM Room Hall B #149
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