Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition-练恒 (香港城市大学)

来源:澳门尼威斯人网站8311点击数:690更新时间:2018-09-11

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


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