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B0696
Title: High-dimensional changepoint detection via a geometrically inspired mapping Authors:  Thomas Grundy - STOR-i Centre for Doctoral Training, Lancaster University (United Kingdom) [presenting]
Rebecca Killick - Lancaster University (United Kingdom)
Gueorgui Mihaylov - Royal Mail GBI Data Science Group (United Kingdom)
Jeremy Bradley - Royal Mail GBI Data Science Group (United Kingdom)
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 the number of time points and dimensions grows 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 the high-dimensional time series to two dimensions. 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 both in the accuracy of detected changepoints and computational efficiency.