This paper briefly reviews the recent research in matrix-variate time series analysis, discusses some new developments, especially for seasonal time series, and demonstrates some applications. A general matrix autoregressive moving-average model is introduced. The paper narrates a simple approach for understanding the model, identifiability issues, and estimation. Real examples are used to illustrate the theory.