Real time anomaly detection and categorisation

Fisch, A.T.M. and Bardwell, L. and Eckley, I.A. (2022) Real time anomaly detection and categorisation. Statistics and Computing, 32 (4). ISSN 0960-3174

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Abstract

The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting. The resultant procedure is capable of real-time analysis and categorisation between baseline and two forms of anomalous structure: point and collective anomalies. Various theoretical properties of the procedure are derived. These, together with an extensive simulation study, highlight that the average run length to false alarm and the average detection delay of the proposed online algorithm are very close to that of the offline version. Experiments on simulated and real data are provided to demonstrate the benefits of the proposed method.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
173131
Deposited By:
Deposited On:
27 Jul 2022 10:15
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 11:38