Rights statement: 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.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Scalable change-point and anomaly detection in cross-correlated data with an application to condition monitoring
AU - Tveten, Martin
AU - Eckley, Idris
AU - Fearnhead, Paul
N1 - 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.
PY - 2022/6/30
Y1 - 2022/6/30
N2 - 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.
AB - 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.
KW - Anomaly
KW - Binary Quadratic Programme
KW - Changepoints
KW - Cross-correlation
KW - Outliers
U2 - 10.1214/21-AOAS1508
DO - 10.1214/21-AOAS1508
M3 - Journal article
VL - 16
SP - 721
EP - 743
JO - Annals of Applied Statistics
JF - Annals of Applied Statistics
SN - 1932-6157
IS - 2
ER -