Bayesian model search for nonstationary periodic time series

Beniamino Hadj-Amar, Bärbel Finkenstädt, Mark Fiecas, Francis Lévi, Robert Huckstepp

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behaviour. 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 (MCMC) 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 behaviour 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 of night rest, and the detection of instances of sleep apnea in plethysmographic respiratory traces.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Oct 23 2018

Keywords

  • Bayesian Spectral Analysis
  • Change-Points
  • Reversible-Jump MCMC
  • Sleep Apnea
  • Ultradian Sleep Cycles

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