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June 2022 Scalable change-point and anomaly detection in cross-correlated data with an application to condition monitoring
Martin Tveten, Idris A. Eckley, Paul Fearnhead
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Ann. Appl. Stat. 16(2): 721-743 (June 2022). DOI: 10.1214/21-AOAS1508


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.

Funding Statement

Martin Tveten was supported by the Norwegian Research Council, project 237718 (Big Insight), while Idris A. Eckley and Paul Fearnhead were partly supported by EPSRC grant EP/N031938/1 (StatScale).


We are grateful to OneSubsea for sharing their data with us and to Alex Fisch and Daniel Grose for helpful discussions.


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Martin Tveten. Idris A. Eckley. Paul Fearnhead. "Scalable change-point and anomaly detection in cross-correlated data with an application to condition monitoring." Ann. Appl. Stat. 16 (2) 721 - 743, June 2022.


Received: 1 October 2020; Revised: 1 June 2021; Published: June 2022
First available in Project Euclid: 13 June 2022

Digital Object Identifier: 10.1214/21-AOAS1508

Keywords: anomaly , binary quadratic programme , Change points , cross-correlation , Outliers

Rights: Copyright © 2022 Institute of Mathematical Statistics


Vol.16 • No. 2 • June 2022
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