Modeling matrix variate time series via hidden Markov models with skewed emissions

Data collected today have increasingly become more complex and cannot be analyzed using regular statistical methods. Matrix variate time series data is one such example where the observations in the time series are matrices. Herein, we introduce a set of three hidden Markov models using skewed matrix variate emission distributions for modeling matrix variate time series data. Compared to the hidden Markov model with matrix variate normal emissions, the proposed models present greater flexibility and are capable of modeling skewness in time series data. Parameter estimation is performed using an expectation maximization algorithm. We then look at both simulated data and salary data for public Texas universities.