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[2112.15423] Modelling matrix time series via a tensor CP-decomposition
Source: arxiv.org
Article summary:
1. The article proposes a new one-pass estimation procedure for modelling matrix time series based on a tensor CP-decomposition.
2. A refined approach is proposed to improve the finite-sample performance of the estimation.
3. Asymptotic theory has been established under a general setting without stationarity, showing that all component coefficient vectors in the CP-decomposition are estimated consistently with certain convergence rates.
Article analysis:
The article is written in an objective and unbiased manner, presenting both sides of the argument equally and providing evidence for its claims. The authors have provided detailed explanations of their proposed method and have also provided simulations and real data to illustrate its effectiveness. Furthermore, they have established asymptotic theory under a general setting without stationarity, which shows that all component coefficient vectors in the CP-decomposition are estimated consistently with certain convergence rates.
The only potential issue with the article is that it does not explore any counterarguments or alternative methods for modelling matrix time series, which could be beneficial for readers who are looking for more comprehensive information on this topic.