Testing for the Presence of Correlation Changes in a Multivariate Time Series: A Permutation Based Approach.

ABSTRACT: Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than one change point underlying the data. To deal with this drawback, we propose a permutation based significance test for Kernel Change Point (KCP) detection on the running correlations. Given a requested number of change points K, KCP divides the time series into K + 1 phases by minimizing the within-phase variance. The new permutation test looks at how the average within-phase variance decreases when K increases and compares this to the results for

SUBMITTER: Cabrieto J 

PROVIDER: S-EPMC5768740 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature