A0941
Title: Modelling matrix time series via a tensor CP-decomposition
Authors: Jinyuan Chang - Southwestern University of Finance and Economics (China)
Jing He - Southwestern University of Finance and Economics (China) [presenting]
Lin Yang - Southwestern University of Finance and Economics (China)
Qiwei Yao - London School of Economics (UK)
Abstract: The purpose is to model matrix time series based on a tensor canonical polyadic (CP)-decomposition. Instead of using an iterative algorithm, which is the standard practice for estimating CP-decompositions, a new one-pass estimation procedure is proposed based on a generalized eigenanalysis constructed from the serial dependence structure of the underlying process. To overcome the intricacy of solving a rank-reduced generalized eigenequation, a further refined approach is proposed, which projects it into a lower dimensional full-ranked eigenequation. This refined method can significantly improve the finite-sample performance. It is shown that all the component coefficient vectors in the CP-decomposition can be estimated consistently. The proposed model and the estimation method are also illustrated with both simulated and real data, showing effective dimension reduction in modelling and forecasting matrix time series.
