学术报告

Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition

阅读次数:205

题目:Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition
报告人:连恒(香港城市大学)
时间:2018年11月7日10:00-11:00
地点:致远楼101室
Abstract: 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.

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