Factor Models for High-Dimensional Tensor Time Series

Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern collection capabilities. Often such observations are taken over time, forming time series. In this article we present factor model approach the analysis high-dimensional dynamic series and multi-category transport networks. This presents two estimation procedures along with their theoretical properties simulation results. We applications illustrate its interpretations.