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Cluster Analysis of Time Series via Kendall Distribution

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Strengthening Links Between Data Analysis and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 315))

Abstract

We present a method to cluster time series according to the calculation of the pairwise Kendall distribution function between them. A case study with environmental data illustrates the introduced methodology.

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Author information

Authors and Affiliations

  1. School of Economics and Management, Free University of Bozen–Bolzano, Bolzano, Italy

    Fabrizio Durante & Roberta Pappadà

Authors
  1. Fabrizio Durante
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  2. Roberta Pappadà
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Corresponding author

Correspondence to Fabrizio Durante .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Faculty of Mathematics and Information Sciences, Systems Research Institute and Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Grzegorzewski

  2. Polish Academy of Sciences, Faculty of Mathematics and Information Sciences, Systems Research Institute and Warsaw University of Technology, Warsaw, Poland

    Marek Gagolewski

  3. Polish Academy of Sciences Systems Research Institute, Warsaw, Poland

    Olgierd Hryniewicz

  4. Departamento de Estadistica e I.O. y D.M., University of Oviedo, Oviedo, Spain

    María Ángeles Gil

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Durante, F., Pappadà, R. (2015). Cluster Analysis of Time Series via Kendall Distribution. In: Grzegorzewski, P., Gagolewski, M., Hryniewicz, O., Gil, M. (eds) Strengthening Links Between Data Analysis and Soft Computing. Advances in Intelligent Systems and Computing, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-10765-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-10765-3_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10764-6

  • Online ISBN: 978-3-319-10765-3

  • eBook Packages: EngineeringEngineering (R0)

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