报告题目: Factor Modelling for Clustering High-dimensional Time Series 

报告人:  潘光明 教授  （新加坡南洋理工大学）

报告时间: 2022年11月3日(周四) 15:00-17:00

报告地点: 腾讯会议 ID： 211 199 855

报告摘要: We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao (2012). 

This is a joint work with Zhang Bo, Yao Qiwei and Zhou Wang.

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