Bayesian Model Search for Nonstationary Periodic Time Series

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

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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 of night rest, and the detection of instances of sleep apnea in plethysmographic respiratory traces. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1320-1335
Number of pages16
JournalJournal of the American Statistical Association
Issue number531
StatePublished - Jul 2 2020

Bibliographical note

Publisher Copyright:
© 2019 The Author(s). Published with license by Taylor & Francis Group, LLC.


  • Bayesian spectral analysis
  • Change-points
  • Reversible-jump MCMC
  • Sleep apnea
  • Ultradian sleep cycles


Dive into the research topics of 'Bayesian Model Search for Nonstationary Periodic Time Series'. Together they form a unique fingerprint.

Cite this