BayesProject : Fast computation of a projection direction for multivariate changepoint detection

Hahn, Georg and Fearnhead, Paul and Eckley, Idris (2020) BayesProject : Fast computation of a projection direction for multivariate changepoint detection. Statistics and Computing, 30. 1691–1705. ISSN 0960-3174

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This article focuses on the challenging problem of efficiently detecting changes in mean within multivariate data sequences. Multivariate changepoints can be detected by projecting a multivariate series to a univariate one using a suitable projection direction that preserves a maximal proportion of signal information. However, for some existing approaches the computation of such a projection direction can scale unfavourably with the number of series and might rely on additional assumptions on the data sequences, thus limiting their generality. We introduce BayesProject, a computationally inexpensive Bayesian approach to compute a projection direction in such a setting. The proposed approach allows the incorporation of prior knowledge of the changepoint scenario, when such information is available, which can help to increase the accuracy of the method. A simulation study shows that BayesProject is robust, yields projections close to the oracle projection direction and, moreover, that its accuracy in detecting changepoints is comparable to, or better than, existing algorithms while scaling linearly with the number of series.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
?? multivariate data sequencesegmentationdimension reductionstructural breakbreakpointcusumcomputational theory and mathematicstheoretical computer sciencestatistics and probabilitystatistics, probability and uncertainty ??
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Deposited On:
11 Aug 2020 08:20
Last Modified:
21 Dec 2023 08:20