Factor Modeling for Clustering High-Dimensional Time Series

• 成果类型: Article
• 署名作者: Zhang, Bo; Pan, Guangming; Yao, Qiwei; Zhou, Wang
• 署名单位: Chinese Academy of Sciences; University of Science & Technology of China, CAS; Nanyang Technological University; University of London; London School Economics & Political Science; National University of Singapore
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2183132
• 发表日期: 2024
• 页码: 1252-1263
• 关键词: regression-coefficients; confidence-regions; breast-cancer; tests; projections; predictor

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, 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. for this article are available online.