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Modelling matrix time series via a tensor CP-decomposition

Chang, Jinyuan, Zhang, Henry, Yang, Lin and Yao, Qiwei ORCID: 0000-0003-2065-8486 (2023) Modelling matrix time series via a tensor CP-decomposition. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85 (1). 127 – 148. ISSN 1369-7412

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Identification Number: 10.1093/jrsssb/qkac011


We consider 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, we propose a new and one-pass estimation procedure 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, we propose a further refined approach which projects it into a lower-dimensional full-ranked eigenequation. This refined method can significantly improve the finite-sample performance. We show 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.

Item Type: Article
Official URL:
Additional Information: © 2023 (RSS) Royal Statistical Society.
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 16 Dec 2022 16:48
Last Modified: 27 Feb 2024 22:36

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