主 题:Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition
内容简介:The VAR model involves a large number of parameters so it can suffer from the curse of dimensionality for high-dimensional time series data. The reduced-rank coefficient model can alleviate the problem but the low-rank structure along the time direction for time series models has never been considered. We rearrange the parameters in the VAR model to a tensor form, and propose a multilinear low-rank VAR model via tensor decomposition that effectively exploits the temporal and cross-sectional low-rank structure. Effectiveness of the methods is demonstrated on simulated and real data.
报告人:练恒 副教授
时 间:2018-09-14 15:30
地 点:竞慧东楼302
举办单位:统计与数学学院 澄园书院