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.
Cite
Text
Knoblauch and Damoulas. "Spatio-Temporal Bayesian On-Line Changepoint Detection with Model Selection." International Conference on Machine Learning, 2018.Markdown
[Knoblauch and Damoulas. "Spatio-Temporal Bayesian On-Line Changepoint Detection with Model Selection." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/knoblauch2018icml-spatiotemporal/)BibTeX
@inproceedings{knoblauch2018icml-spatiotemporal,
title = {{Spatio-Temporal Bayesian On-Line Changepoint Detection with Model Selection}},
author = {Knoblauch, Jeremias and Damoulas, Theodoros},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {2718-2727},
volume = {80},
url = {https://mlanthology.org/icml/2018/knoblauch2018icml-spatiotemporal/}
}