A primer on coupled state-switching models for multiple interacting time series. (June 2021)

• Record Type: Journal Article
• Authors: Pohle, Jennifer; Langrock, Roland; Schaar, Mihaela van der; King, Ruth; Jensen, Frants Havmand
• Is Part Of: Statistical modelling. Volume 21:Number 3(2021)
• Page Start: 264
• Page End: 285
• Publication Date: 2021-06
• Subjects: hidden Markov model -- Time series -- Markov-switching regression -- animal movement -- disease progression; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X20956423
• Languages: English
• ISSNs: 1471-082X

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.