Factor modelling for clustering high-dimensional time series

Bo Zhang, Guangming Pan, Qiwei Yao and Jian-Zhou Wang

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: eigenanalysis; idiosyncratic components; k-means clustering algorithm; strong and weak factors (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2023-04-05
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

Published in Journal of the American Statistical Association, 5, April, 2023. ISSN: 0162-1459

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:118186

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