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Factor modelling for clustering high-dimensional time series

Author

Listed:
  • Zhang, Bo
  • Pan, Guangming
  • Yao, Qiwei
  • Wang, Jian-Zhou

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.

Suggested Citation

  • Zhang, Bo & Pan, Guangming & Yao, Qiwei & Wang, Jian-Zhou, 2023. "Factor modelling for clustering high-dimensional time series," LSE Research Online Documents on Economics 118186, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:118186
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    File URL: http://eprints.lse.ac.uk/118186/
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    Cited by:

    1. Junfan Mao & Zhigen Gao & Bing-Yi Jing & Jianhua Guo, 2024. "On the statistical analysis of high-dimensional factor models," Statistical Papers, Springer, vol. 65(8), pages 4991-5019, October.

    More about this item

    Keywords

    eigenanalysis; idiosyncratic components; k-means clustering algorithm; strong and weak factors;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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