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


State-switching models such as hidden Markov models or Markovswitching 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 paper, 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
Number of pages36
JournalStatistical modelling
Early online date21 Oct 2020
Publication statusE-pub ahead of print - 21 Oct 2020


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