Sequential Bayesian Prediction in the Presence of Changepoints and Faults

Journal article

We introduce a new sequential algorithm for making robust predictions in the presence of changepoints. Unlike previous approaches, which focus on the problem of detecting and locating changepoints, our algorithm focuses on the problem of making predictions even when such changes might be present. We introduce nonstationary covariance functions to be used in Gaussianprocess prediction that model such changes, then proceed to demonstrate how to effectively manage the hyperparameters associated with those covariance functions. By using Bayesian quadrature, we can integrateout the hyperparameters, allowing us to calculate the marginal predictive distribution. Furthermore, if desired, the posterior distribution over putative changepoint locations can be calculated as a natural byproduct ofour prediction algorithm. Copyright 2009.

Authors: Garnett, R; Osborne, M; Reece, S; Rogers, A; Roberts, S

• Publication status: Published
• Journal: COMPUTER JOURNAL
• Volume: 53
• Issue: 9
• Pages: 1430-1446
• Publication date: 2010-01-01
• DOI: 10.1093/comjnl/bxq003
• EISSN: 1460-2067
• ISSN: 0010-4620
• Language: English
• Keywords: changepoint detection; Gaussian processes; Bayesian methods; time-series prediction; fault detection