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<p>This paper proposes clustering methods for large-scale stationary time series using a fuzzy approach. Adopting partitioning around centroids (PAC) and partitioning around medoids (PAM), and focusing on distributional properties of individual series, we classify a large set of time series by transforming the series into probability density functions via nonparametric density estimation, such as the kernel estimation, and using a proper distance measure, such as the Hellinger distance, between density functions. We use simulations and two real applications to demonstrate the good performance and effectiveness of the proposed clustering methods in finite samples. The proposed methods are also applicable to the spectral density functions if one focuses on the serial dependence of individual series.</p>

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Clustering Large-scale Time Series

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月27日

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  • Pierpaolo D’Urso,
  • Livia De Giovanni,
  • Ruey S. Tsay,
  • Vincenzina Vitale

摘要

This paper proposes clustering methods for large-scale stationary time series using a fuzzy approach. Adopting partitioning around centroids (PAC) and partitioning around medoids (PAM), and focusing on distributional properties of individual series, we classify a large set of time series by transforming the series into probability density functions via nonparametric density estimation, such as the kernel estimation, and using a proper distance measure, such as the Hellinger distance, between density functions. We use simulations and two real applications to demonstrate the good performance and effectiveness of the proposed clustering methods in finite samples. The proposed methods are also applicable to the spectral density functions if one focuses on the serial dependence of individual series.