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

Abstract

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.

arXiv comment10 pages, 7f figures, to appear in Proceedings of the 35th International Conference on Machine Learning 2018

References in corpus (3)

• Bayesian Online Changepoint Detection
• Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
• Generalized Direct Change Estimation in Ising Model Structure

Cited by in corpus (7)

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• WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data
• Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
• Bayesian Online Prediction of Change Points
• Continuous Meta-Learning without Tasks
• Latent Neural Stochastic Differential Equations for Change Point Detection
• Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning