Abstract

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.

中文翻译：

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高维张量时间序列的因子模型

摘要

由于现代数据收集能力，大张量（多维数组）数据现在经常出现在广泛的应用中。通常这样的观察是随着时间的推移进行的，形成张量时间序列。在本文中，我们提出了一种因子模型方法来分析高维动态张量时间序列和多类别动态传输网络。本文介绍了两种估计程序及其理论特性和模拟结果。我们提出了两个应用程序来说明模型及其解释。