SCALABLE CHANGE-POINT AND ANOMALY DETECTION IN CROSS-CORRELATED DATA WITH AN APPLICATION TO CONDITION MONITORING

• 成果类型: Article
• 署名作者: Tveten, Martin; Eckley, Idris A.; Fearnhead, Paul
• 署名单位: University of Oslo; Lancaster University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1508
• 发表日期: 2022
• 页码: 721-743
• 关键词: time-series; SPARSE

摘要

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.