Matrix‐variate time series modelling with hidden Markov models
Abdullah Asilkalkan, Xuwen Zhu · Stat · 2021
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.