Factor Models for High-Dimensional Tensor Time Series

Understand

Large tensor (multi-dimensional array) data are now routinely collected 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 paper we present a factor model approach for analyzing high-dimensional dynamic tensor time series and multi-category dynamic transport networks.
• Two estimation procedures along with their theoretical properties and simulation results are presented.