Factor Models for High-Dimensional Tensor Time Series
Factor Models for High-Dimensional Tensor Time Series
复制标题
高维张量时间序列的因子模型
DOI：
10.1080/01621459.2021.1912757
复制
发表时间：
2021-05-18
影响因子：
3.7
通讯作者：
Zhang, Cun-Hui
中科院分区：
文献类型：
--
作者：
Chen, Rong;Yang, Dan;Zhang, Cun-Hui
由于现代数据收集能力的发展，大张量（多维数组）数据经常出现在广泛的应用中。通常这种观测是随时间推移的，形成张量时间序列。本文提出了一种分析高维动态张量时间序列和多类别动态运输网络的因子模型方法。本文介绍了两种估计方法及其理论性质和仿真结果。我们提出了两个应用来说明该模型及其解释。
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.