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Clustering Time Series by Nonlinear Dependence

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Michele La Rocca

    (University of Salerno)

  • Luca Vitale

    (University of Salerno)

Abstract

The problem of time series clustering has attracted growing research interest in the last decade. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation, in this paper, we study clustering methods applicable to time series with a general dependent (possibly nonlinear) structure. We propose a dissimilarity measure based on the auto distance correlation function which is able to detect both linear and nonlinear dependence structures. Once the pairwise dissimilarity matrix for time series has been obtained, a standard clustering algorithm, such as hierarchical clustering algorithm, can be used. Numerical studies based on Monte Carlo experiments show that our method performs reasonably well.

Suggested Citation

  • Michele La Rocca & Luca Vitale, 2021. "Clustering Time Series by Nonlinear Dependence," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 291-297, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_43
    DOI: 10.1007/978-3-030-78965-7_43
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