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Feature Extraction over Multiple Representations for Time Series Classification

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New Frontiers in Mining Complex Patterns (NFMCP 2013)

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Abstract

We suggest a simple yet effective and parameter-free feature construction process for time series classification. Our process is decomposed in three steps: (i) we transform original data into several simple representations; (ii) on each representation, we apply a coclustering method; (iii) we use coclustering results to build new features for time series. It results in a new transactional (i.e. object-attribute oriented) data set, made of time series identifiers described by features related to the various generated representations. We show that a Selective Naive Bayes classifier on this new data set is highly competitive when compared with state-of-the-art times series classification methods while highlighting interpretable and class relevant patterns.

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Notes

  1. 1.

    In contrast to lazy learning, eager learning has an explicit training phase and generally deploys faster.

  2. 2.

    khc and snb are both available at http://www.khiops.com.

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Acknowledgments

We wish to thank Anthony Bagnall and his team from University of East-Anglia for providing tsc-ensemble prototype, Eamonn Keogh and his team from University of California Riverside for providing prototypes of dtw-nn and fast-shapelets.

Author information

Authors and Affiliations

  1. Orange Labs, 2, Avenue Pierre Marzin, 22307, Lannion Cedex, France

    Dominique Gay, Romain Guigourès, Marc Boullé & Fabrice Clérot

Authors
  1. Dominique Gay
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  2. Romain Guigourès
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  3. Marc Boullé
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  4. Fabrice Clérot
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Corresponding authors

Correspondence to Dominique Gay or Marc Boullé .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR, CNR, Rende, Italy

    Giuseppe Manco

  5. Rende, Italy

    Elio Masciari

  6. Department of Computer Science, University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

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Gay, D., Guigourès, R., Boullé, M., Clérot, F. (2014). Feature Extraction over Multiple Representations for Time Series Classification. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-08407-7_2

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  • Online ISBN: 978-3-319-08407-7

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