Statistical Modelling 21 (3) (2021), 264–285
A primer on coupled state-switching models for multiple interacting time series
Jennifer Pohle,
Bielefeld University,
Bielefeld,
Germany
e-mail: jennifer.pohle@uni-bielefeld.de
Roland Langrock,
Bielefeld University,
Bielefeld,
Germany
Mihaela van der Schaar,
University of Cambridge,
Cambridge,
UK
and
The Alan Turing Institute,
London, UK
and
University of California,
Los Angeles,
California, USA
Ruth King,
The Alan Turing Institute,
London, UK
and
University of Edinburgh,
Edinburgh, UK
Frants Havmand Jensen,
Woods Hole Oceanographic Institution,
Falmouth, Massachusetts,
USA
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.
Keywords:
hidden Markov model; Time series; Markov-switching regression; animal movement; disease progression.
Downloads:
Example data and code in
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