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20191022 练恒:Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition
时间:2019-10-22

报告时间:20191022日  下午15:30-16:30

报告地点:明德主楼1016

报告题目:Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition


报告摘要: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.

 

报告人简介:练恒,现任香港城市大学数学系副教授,于2000年在中国科学技术大学获得数学和计算机学士学位,2007年在美国布朗大学获得计算机硕士,经济学硕士和应用数学博士学位。研究方向包括高维数据分析,函数数据分析,机器学习等。在《Journal of the Royal Statistical SocietySeries B》、《Journal of the American Statistical Association》等国际期刊上发表高水平学术论文30多篇。