A primer on coupled state-switching models for multiple interacting time series

Jennifer Pohle, Roland Langrock, Mihaela van der Schaar, Ruth King, Frants Havmand Jensen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying non-observable states. Coupled state-switching models extend these approaches to address the case of multiple observation sequences whose underlying state variables interact. In this article, we provide an overview of the modelling techniques related to coupling in state-switching models, thereby forming a rich and flexible statistical framework particularly useful for modelling correlated time series. Simulation experiments demonstrate the relevance of being able to account for an asynchronous evolution as well as interactions between the underlying latent processes. The models are further illustrated using two case studies related to (a) interactions between a dolphin mother and her calf as inferred from movement data and (b) electronic health record data collected on 696 patients within an intensive care unit.

Original languageEnglish (US)
Pages (from-to)264-285
Number of pages22
JournalStatistical Modelling
Volume21
Issue number3
DOIs
StatePublished - Jun 2021
Externally publishedYes

Keywords

  • animal movement
  • disease progression
  • hidden Markov model
  • Markov-switching regression
  • Time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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