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Ordinal Versus Nominal Time Series Classification

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Advanced Analytics and Learning on Temporal Data (AALTD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12588))

Abstract

Time series ordinal classification is one of the less studied problems in time series data mining. This problem consists in classifying time series with labels that show a natural order between them. In this paper, an approach is proposed based on the Shapelet Transform (ST) specifically adapted to ordinal classification. ST consists of two different steps: 1) the shapelet extraction procedure and its evaluation; and 2) the classifier learning using the transformed dataset. In this way, regarding the first step, 3 ordinal shapelet quality measures are proposed to assess the shapelets extracted, and, for the second step, an ordinal classifier is applied once the transformed dataset has been constructed. An empirical evaluation is carried out, considering 7 ordinal datasets from the UEA & UCR Time Series Classification (TSC) repository. The results show that a support vector ordinal classifier applied to the ST using the Pearson’s correlation coefficient (\(R^2\)) is the combination achieving the best results in terms of two evaluation metrics: accuracy and average mean absolute error. A final comparison against three of the most popular and competitive nominal TSC techniques is performed, demonstrating that ordinal approaches can achieve higher performances even in terms of accuracy.

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Notes

  1. 1.

    ORCA is available in the repository https://github.com/ayrna/orca.

  2. 2.

    All the code used in this paper is available in the repository https://github.com/dguijo/TSOC.

  3. 3.

    http://www.timeseriesclassification.com/.

  4. 4.

    sktime is available in the repository https://github.com/alan-turing-institute/sktime.

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Acknowledgement

This research has been partially supported by the “Ministerio de Economía, Industria y Competitividad” (Ref. TIN2017-85887-C2-1-P) and the “Fondo Europeo de Desarrollo Regional (FEDER) y de la Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía” (Ref. UCO-1261651), Spain. D. Guijo-Rubio’s research has been supported by the FPU Predoctoral Program from Spanish Ministry of Education and Science (Grant Ref. FPU16/02128).

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Authors and Affiliations

  1. Department of Computer Sciences, Universidad de Córdoba, Córdoba, Spain

    David Guijo-Rubio, Pedro Antonio Gutiérrez & César Hervás-Martínez

  2. School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK

    Anthony Bagnall

Authors
  1. David Guijo-Rubio
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  2. Pedro Antonio Gutiérrez
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  3. Anthony Bagnall
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  4. César Hervás-Martínez
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Corresponding author

Correspondence to David Guijo-Rubio .

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Editors and Affiliations

  1. Orange Labs, Lannion, France

    Vincent Lemaire

  2. Inria, University of Rennes, Rennes, France

    Simon Malinowski

  3. University of East Anglia, Norwich, UK

    Anthony Bagnall

  4. Agrocampus Ouest/IRISA, Rennes, France

    Thomas Guyet

  5. CNRS, LETG/IRISA, University of Rennes 2, Rennes, France

    Romain Tavenard

  6. University College Dublin, Dublin, Ireland

    Georgiana Ifrim

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Guijo-Rubio, D., Gutiérrez, P.A., Bagnall, A., Hervás-Martínez, C. (2020). Ordinal Versus Nominal Time Series Classification. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2020. Lecture Notes in Computer Science(), vol 12588. Springer, Cham. https://doi.org/10.1007/978-3-030-65742-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-65742-0_2

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