This paper briefly reviews the recent research in matrix‐variate time series analysis,
discusses some new developments, especially for seasonal time series, and demonstrates …

Many data sets in biology, medicine, and other biostatistical areas deal with matrix‐valued
time series. The case of a single univariate time series is very well developed in the …

In this article, we develop additive autoregressive models (Add‐ARM) for the time series
data with matrix valued predictors. The proposed models assume separable row, column …

We propose a novel cointegrated autoregressive model for matrix-valued time series, with bi-
linear cointegrating vectors corresponding to the rows and columns of the matrix data …

Matrix time series, which consist of matrix-valued data observed over time, are prevalent in
various fields such as economics, finance, and engineering. Such matrix time series data …

Tensor and matrix time series data have been amassed more and more from many areas in
recent years, calling for new statistical models, methods and algorithms for analyzing such …

We propose a contemporaneous bilinear transformation for ap× q matrix time series to
alleviate the difficulties in modeling and forecasting matrix time series when p and/or q are …

Reduced-rank regressions are powerful tools used to identify co-movements within
economic time series. However, this task becomes challenging when we observe matrix …

Matrix-variate time series data are largely available in applications. However, no attempt has
been made to study their conditional heteroscedasticity that is often observed in economic …

Matrix-valued time series data are widely available in various applications, attracting
increasing attention in the literature. However, while nonlinearity has been recognized, the …