Journal
COMPUTER JOURNAL
Volume 53, Issue 9, Pages 1430-1446Publisher
OXFORD UNIV PRESS
DOI: 10.1093/comjnl/bxq003
Keywords
Gaussian processes; time-series prediction; changepoint detection; fault detection; Bayesian methods
Categories
Funding
- BAE Systems
- EPSRC [EP/C548051/1]
- Engineering and Physical Sciences Research Council [EP/C548051/1] Funding Source: researchfish
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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 Gaussian process prediction that model such changes, and then proceed to demonstrate how to effectively manage the hyperparameters associated with those covariance functions. We further introduce covariance functions to be used in situations where our observation model undergoes changes, as is the case for sensor faults. By using Bayesian quadrature, we can integrate out the hyperparameters, allowing us to calculate the full marginal predictive distribution. Furthermore, if desired, the posterior distribution over putative changepoint locations can be calculated as a natural byproduct of our prediction algorithm.
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