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High-dimensional, multiscale online changepoint detection

Chen, Y; Wang, T; Samworth, R; (2022) High-dimensional, multiscale online changepoint detection. Journal of the Royal Statistical Society Series B: Statistical Methodology 10.1111/rssb.12447. (In press). Green open access

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Abstract

We introduce a new method for high-dimensional, online changepoint detection in settings where a p-variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of different scales in each coordinate, and then aggregating test statistics across scales and coordinates. The algorithm is online in the sense that both its storage requirements and worst-case computational complexity per new observation are independent of the number of previous observations; in practice, it may even be significantly faster than this. We prove that the patience, or average run length under the null, of our procedure is at least at the desired nominal level, and provide guarantees on its response delay under the alternative that depend on the sparsity of the vector of mean change. Simulations confirm the practical effectiveness of our proposal, which is implemented in the R package ocd, and we also demonstrate its utility on a seismology data set.

Type: Article
Title: High-dimensional, multiscale online changepoint detection
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssb.12447
Publisher version: https://doi.org/10.1111/rssb.12447
Language: English
Additional information: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: High-dimensional changepoint detection; online algorithm; sequential method; average run length; detection delay
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10118506
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