Factor Models for High-Dimensional Tensor Time Series

• 成果类型: Article
• 署名作者: Chen, Rong; Yang, Dan; Zhang, Cun-Hui
• 署名单位: Rutgers University System; Rutgers University New Brunswick; University of Hong Kong
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1912757
• 发表日期: 2022
• 页码: 94-116
• 关键词: Principal component analysis; number; identification; rank; matrices; arbitrage; networks

摘要

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.