Scalable change-point and anomaly detection in cross-correlated data with an application to condition monitoring

Tveten, Martin and Eckley, Idris and Fearnhead, Paul (2022) Scalable change-point and anomaly detection in cross-correlated data with an application to condition monitoring. Annals of Applied Statistics, 16 (2). pp. 721-743. ISSN 1932-6157

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Abstract

Motivated by a condition monitoring application arising from subsea engineering, we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. Given the need to analyse such series efficiently, we explore a computationally efficient approximation of the maximum likelihood solution to the resulting modelling framework and develop a new dynamic programming algorithm for solving the resulting binary quadratic programme when the precision matrix of the time series at any given time point is banded. Through a comprehensive simulation study we show that the resulting methods perform favorably compared to competing methods, both in the anomaly and change detection settings, even when the sparsity structure of the precision matrix estimate is misspecified. We also demonstrate its ability to correctly detect faulty time periods of a pump within the motivating application.

Item Type:
Journal Article
Journal or Publication Title:
Annals of Applied Statistics
Additional Information:
The final, definitive version of this article has been published in the Journal, Annals of Applied Statistics, 16 (2), pp 721-743 2022, © 2022 Cambridge University Press.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
163822
Deposited By:
Deposited On:
04 Jan 2022 14:25
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 10:57