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

Automated summary

This article introduces a factor model approach for analyzing high-dimensional dynamic tensor time series and multi-category dynamic transport networks, presenting two estimation procedures, their theoretical properties, and simulation results. Two applications are provided to illustrate the model and its interpretations.