High-Dimensional Changepoint Detection via a Geometrically Inspired Mapping

Grundy, Tom and Killick, Rebecca and Mihaylov, G (2020) High-Dimensional Changepoint Detection via a Geometrically Inspired Mapping. Statistics and Computing, 30. 1155–1166. ISSN 0960-3174

Full text not available from this repository.

Abstract

High-dimensional changepoint analysis is a growing area of research and has applications in a wide range of fields. The aim is to accurately and efficiently detect changepoints in time series data when both the number of time points and dimensions grow large. Existing methods typically aggregate or project the data to a smaller number of dimensions, usually one. We present a high-dimensional changepoint detection method that takes inspiration from geometry to map a high-dimensional time series to two dimensions. We show theoretically and through simulation that if the input series is Gaussian, then the mappings preserve the Gaussianity of the data. Applying univariate changepoint detection methods to both mapped series allows the detection of changepoints that correspond to changes in the mean and variance of the original time series. We demonstrate that this approach outperforms the current state-of-the-art multivariate changepoint methods in terms of accuracy of detected changepoints and computational efficiency. We conclude with applications from genetics and finance.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
144438
Deposited By:
Deposited On:
01 Jun 2020 10:45
Refereed?:
Yes
Published?:
Published
Last Modified:
06 Jul 2020 00:57