Factor modelling for clustering high-dimensional time series

Zhang, Bo, Pan, Guangming, Yao, Qiwei ORCID: 0000-0003-2065-8486 and Wang, Jian-Zhou (2023) Factor modelling for clustering high-dimensional time series. Journal of the American Statistical Association. ISSN 0162-1459

Text (Yao_factor-modelling--published) - Published Version Available under License Creative Commons Attribution. Download (2MB)

• Author
• Scopus publication

Abstract

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, and 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. Supplementary materials for this article are available online.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/uasa20
Additional Information:	© 2023 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	16 Feb 2023 09:51
Last Modified:	18 Nov 2024 17:03
URI:	http://eprints.lse.ac.uk/id/eprint/118186

Actions (login required)

View Item