Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
Abstract
In the analysis of sequential data, the detection of abrupt changes is important in predicting future events. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified thresholds for the tests, and use them to improve Bayesian Online Change Point Detection (BOCPD) by confirming statistically significant changes and non-changes. Our Confirmatory BOCPD (CBOCPD) algorithm finds multiple structural breaks in GPs even when hyperparameters are not tuned precisely. We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD. Experimental results on synthetic and real-world datasets show that our proposed algorithm outperforms existing methods for the prediction of nonstationarity in terms of both regression error and log-likelihood.
Cite
Text
Han et al. "Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/340Markdown
[Han et al. "Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/han2019ijcai-confirmatory/) doi:10.24963/IJCAI.2019/340BibTeX
@inproceedings{han2019ijcai-confirmatory,
title = {{Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes}},
author = {Han, Jiyeon and Lee, Kyowoon and Tong, Anh and Choi, Jaesik},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {2449-2455},
doi = {10.24963/IJCAI.2019/340},
url = {https://mlanthology.org/ijcai/2019/han2019ijcai-confirmatory/}
}