Bayesian Model Search for Nonstationary Periodic Time Series.

ABSTRACT: We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behavior. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo algorithm that simultaneously updates the change-points and the periodicities relevant to any segment between them. We show that the proposed methodology successfully identifies time changing oscillatory behavior in two applications which are relevant to e-Health and sleep research, namely the occurrence of ultradian oscillations in human skin temperature during the time o

SUBMITTER: Hadj-Amar B 

PROVIDER: S-EPMC7984273 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature