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Fuzzy Clustering of Series Using Quantile Autocovariances

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Advanced Analysis and Learning on Temporal Data (AALTD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9785))

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Abstract

Unlike conventional clustering, fuzzy cluster analysis allows data elements to belong to more than one cluster by assigning membership degrees of each data to clusters. This work proposes a fuzzy C–medoids algorithm to cluster time series based on comparing their estimated quantile autocovariance functions. The behaviour of the proposed algorithm is studied on different simulated scenarios and its effectiveness is concluded by comparison with alternative approaches. Finally, an application on real data involving series of hourly electricity demand is developed to illustrate the usefulness of the methodology.

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Acknowledgement

The authors wish to thank the two reviewers for their helpful comments and valuable suggestions, which have allowed us to improve the quality of this work. This research was supported by the Spanish grant MTM2014-52876-R from the Ministerio de Economía y Competitividad.

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Authors and Affiliations

  1. Research Group on Modeling, Optimization and Statistical Inference (MODES), Department of Mathematics, Computer Science Faculty, University of A Coruña, A Coruña, Spain

    Borja Lafuente-Rego & Jose A. Vilar

Authors
  1. Borja Lafuente-Rego
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  2. Jose A. Vilar
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Corresponding author

Correspondence to Borja Lafuente-Rego .

Editor information

Editors and Affiliations

  1. Lab. d'Informatique de Grenoble, Université Grenoble, Grenoble, France

    Ahlame Douzal-Chouakria

  2. Universidade da Coruna, Coruna, Spain

    José A. Vilar

  3. IRISA, Université de Bretagne-Sud, Vannes, France

    Pierre-François Marteau

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Lafuente-Rego, B., Vilar, J.A. (2016). Fuzzy Clustering of Series Using Quantile Autocovariances. In: Douzal-Chouakria, A., Vilar, J., Marteau, PF. (eds) Advanced Analysis and Learning on Temporal Data. AALTD 2015. Lecture Notes in Computer Science(), vol 9785. Springer, Cham. https://doi.org/10.1007/978-3-319-44412-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-44412-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44411-6

  • Online ISBN: 978-3-319-44412-3

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