High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition
基于张量分解的高维向量自回归时间序列建模
The classical vector autoregressive model is a fundamental tool for multivariate time series analysis. However, it involves too many parameters when the number of time series and lag order are even moderately large. This article proposes to rearrange the transition matrices of the model into a tensor form such that the parameter space can be restricted along three directions simultaneously via tensor decomposition. In contrast, the reduced-rank regression method can restrict…