Fast Online Changepoint Detection via Functional Pruning CUSUM statistics

Romano, Gaetano and Eckley, Idris and Fearnhead, Paul and Rigaill, Guillem (2021) Fast Online Changepoint Detection via Functional Pruning CUSUM statistics. arxiv.org.

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Abstract

Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorithms have most power to detect. We introduce an algorithm, Functional Online CuSUM (FOCuS), which is equivalent to running these earlier methods simultaneously for all sizes of window, or all possible values for the size of change. Our theoretical results give tight bounds on the expected computational cost per iteration of FOCuS, with this being logarithmic in the number of observations. We show how FOCuS can be applied to a number of different change in mean scenarios, and demonstrate its practical utility through its state-of-the art performance at detecting anomalous behaviour in computer server data.

Item Type:
Journal Article
Journal or Publication Title:
arxiv.org
Subjects:
ID Code:
163723
Deposited By:
Deposited On:
21 Dec 2021 12:25
Refereed?:
No
Published?:
Published
Last Modified:
30 Nov 2022 00:53