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 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.

中文翻译：

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多个交互时间序列的耦合状态切换模型入门

状态转换模型，例如隐马尔可夫模型或马尔可夫转换回归模型，通常用于分析由潜在不可观察状态驱动的观察序列。耦合状态切换模型扩展了这些方法，以解决其潜在状态变量相互作用的多个观察序列的情况。在本文中，我们概述了与状态切换模型中的耦合相关的建模技术，从而形成了一个丰富而灵活的统计框架，特别适用于对相关时间序列进行建模。模拟实验证明了能够解释异步演化以及潜在潜在过程之间的相互作用的相关性。使用两个案例研究进一步说明了这些模型，这些案例研究与 a) 从运动数据推断出的海豚母亲和她的小牛之间的相互作用；b) 在重症监护病房内收集的 696 名患者的电子健康记录数据。