Recently, the paper “Modeling matrix time series via a tensor CP-composition” co-authored by Professor Jinyuan Chang, Associate Professor Jing He, PhD student Lin Yang, and Professor Qiwei Yao from the London School of Economics and Political Science at the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics has been officially accepted by the top international academic journal in statistics, Journal of the Royal Statistical Society Series B.
Abstract
This paper uses tensor CP-decomposition to model matrix time series and proposes using the autocorrelation information of observation sequences to construct generalized eigenvalue equations for estimating CP decomposition. This method is computationally simple and does not require iterations, overcoming the drawback of traditional CP decomposition estimation methods that require multiple iterations to converge. In order to solve the computational uncertainty of solving the non rank generalized eigenvalue equation, this paper also proposes an improved method that transforms it into solving a low dimensional full rank generalized eigenvalue equation, effectively improving the estimation performance under finite samples. Numerical simulation and case analysis show that the newly proposed method has excellent performance in modeling and predicting matrix time series.
Author Introduction
Jinyuan Chang, Executive Director of the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics, Guanghua Distinguished Professor, Doctoral Supervisor, recipient of the National Science Fund for Distinguished Young Scholars of China, Sichuan Province Distinguished Expert, and member of the Sichuan Province Statistical Expert Advisory Committee. Mainly engaged in two research fields: ultra-high dimensional data analysis and high-frequency financial data analysis.
He Jing is an associate professor at the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics, mainly engaged in research in the fields of high-dimensional data analysis and spatiotemporal data analysis.
Yang Lin, a 2020 doctoral student majoring in statistics at the School of Statistics, Southwestern University of Finance and Economics, with Professor Chang Jinyuan as his supervisor. Mainly engaged in research in the fields of ultra-high dimensional time series analysis and functional time series analysis.
Yao Qiwei, Chair Professor at the London School of Economics and Political Science, UK, Fellow of Institute of Mathematical Statistics, Elected member of International Statistical Institute，Fellow of American Statictical Association. Mainly engaged in research in the fields of time series analysis, high-dimensional time series modeling and prediction, dimensionality reduction and factor modeling, dynamic network modeling, spatiotemporal modeling, financial econometrics, and non parametric regression.
