主  題:Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition

內(nèi)容簡(jiǎn)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.

報(bào)告人:練恒    副教授

時(shí)  間:2018-09-14    15:30

地  點(diǎn):競(jìng)慧東樓302

舉辦單位:統(tǒng)計(jì)與數(shù)學(xué)學(xué)院  澄園書(shū)院