TY - JOUR

T1 - BayesProject

T2 - Fast computation of a projection direction for multivariate changepoint detection

AU - Hahn, Georg

AU - Fearnhead, Paul

AU - Eckley, Idris

PY - 2020/11/1

Y1 - 2020/11/1

N2 - 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.

AB - 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.

KW - Multivariate data sequence

KW - Segmentation

KW - Dimension reduction

KW - Structural Break

KW - Breakpoint

KW - Cusum

U2 - 10.1007/s11222-020-09966-2

DO - 10.1007/s11222-020-09966-2

M3 - Journal article

VL - 30

SP - 1691

EP - 1705

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

ER -