In this paper, a hidden Markov model for modelling matrix-variate time series data is developed. It relies on matrix-variate distribution and presents a promising alternative to the existing methods. Simulation study is carefully conducted and uses benchmark tests with pre-specified overlapping values. Compared with the existing methods, the proposed model demonstrates rather high accuracy in state classification. Results suggest that such an approach is indeed competitive. Interesting applications are presented for real-life data illustration.

中文翻译：

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使用隐马尔可夫模型进行矩阵变量时间序列建模

在本文中，开发了一种用于建模矩阵变量时间序列数据的隐马尔可夫模型。它依赖于矩阵变量分布，并为现有方法提供了一种有前途的替代方案。模拟研究经过仔细进行，并使用具有预先指定重叠值的基准测试。与现有方法相比，所提出的模型在状态分类方面表现出较高的准确性。结果表明，这种方法确实具有竞争力。展示了有趣的应用程序，用于现实生活中的数据说明。