A0386
Title: Factor modelling for clustering high-dimensional time series
Authors: Bo Zhang - University of Science and Technology of China (China) [presenting]
Abstract: A new unsupervised learning method is proposed 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 all the time series concerned. The 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, and the latent clusters. A numerical illustration with both simulated data and a real data example is also reported. As a spin-off, the proposed new approach also significantly advances the statistical inference for a previous factor model.
