Subset Multivariate Collective And Point Anomaly Detection

Fisch, Alex and Eckley, Idris and Fearnhead, Paul (2021) Subset Multivariate Collective And Point Anomaly Detection. Journal of Computational and Graphical Statistics. ISSN 1061-8600 (In Press)

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Abstract

In recent years, there has been a growing interest in identifying anomalous structure within multivariate data sequences. We consider the problem of detecting collective anomalies, corresponding to intervals where one, or more, of the data sequences behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data sequences, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach is shown to consistently estimate the number and location of the collective anomalies -- a property that has not previously been shown for competing methods. Our approach can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Computational and Graphical Statistics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
136532
Deposited By:
Deposited On:
06 Sep 2019 14:00
Refereed?:
Yes
Published?:
In Press
Last Modified:
24 Oct 2021 05:25