Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Machine Learning
2018-06-07 v2 Machine Learning
Methodology
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.
Cite
@article{arxiv.1805.05383,
title = {Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection},
author = {Jeremias Knoblauch and Theodoros Damoulas},
journal= {arXiv preprint arXiv:1805.05383},
year = {2018}
}
Comments
10 pages, 7f figures, to appear in Proceedings of the 35th International Conference on Machine Learning 2018