Unsupervised Offline Changepoint Detection Ensembles

Applied Sciences

Ivan Maksimov¹  Victor Lobachev²  Iurii Katser²  Viacheslav Kozitsin² 

[1] Group of AI, Cifrum, Rosatom State Atomic Energy Corporation, 119017 Moscow, Russia;Skolkovo Institute of Science and Technology, 143026 Moscow, Russia;

关键词: anomaly detection;    changepoint detection;    ensemble;    time series;    signal processing;    signal segmentation;   

DOI  :  10.3390/app11094280

来源: DOAJ

【 摘 要 】

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.

【 授权许可】

Unknown