Detecting long-lived autodependency changes in a multivariate system via change point detection and regime switching models.

ABSTRACT: Long-lived simultaneous changes in the autodependency of dynamic system variables characterize crucial events as epileptic seizures and volcanic eruptions and are expected to precede psychiatric conditions. To understand and predict such phenomena, methods are needed that detect such changes in multivariate time series. We put forward two methods: First, we propose KCP-AR, a novel adaptation of the general-purpose KCP (Kernel Change Point) method. Whereas KCP is implemented on the raw data and does not shed light on which parameter changed, KCP-AR is applied to the running autocorrelations, allowing to focus on changes in this parameter. Second, we revisit the regime switching AR(1) approach and propose to fit models wherein only the parameters capturing autodependency differ across the re

SUBMITTER: Cabrieto J 

PROVIDER: S-EPMC6199340 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature