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Classification for Time Series Data. An Unsupervised Approach Based on Reduction of Dimensionality

Author

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  • M. Isabel Landaluce-Calvo

    (Universidad de Burgos)

  • Juan I. Modroño-Herrán

    (University of the Basque Country UPV/EHU)

Abstract

In this work we use a novel methodology for the classification of time series data, through a natural, unsupervised data learning process. This strategy is based on the sequential use of Multiple Factor Analysis and an ascending Hierarchical Classification Analysis. These two exploratory techniques complement each other and allow for a clustering of the series based on their time paths and on the reduction of the original dimensionality of the data. The extensive set of graphic and numerical tools available for both methods leads to an exhaustive and rigorous visual and metric analysis of the different trajectories, including their differences and similarities, which will turn out to be responsible of the classes ultimately obtained. An application from Finance, used previously in the literature, highlights the versatility and suitability of this approach.

Suggested Citation

  • M. Isabel Landaluce-Calvo & Juan I. Modroño-Herrán, 2020. "Classification for Time Series Data. An Unsupervised Approach Based on Reduction of Dimensionality," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 380-398, July.
    • Handle: RePEc:spr:jclass:v:37:y:2020:i:2:d:10.1007_s00357-019-9308-z
    DOI: 10.1007/s00357-019-9308-z
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    References listed on IDEAS

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    1. Escofier, B. & Pages, J., 1994. "Multiple factor analysis (AFMULT package)," Computational Statistics & Data Analysis, Elsevier, vol. 18(1), pages 121-140, August.
    2. Juan Vilar & José Vilar & Sonia Pértega, 2009. "Classifying Time Series Data: A Nonparametric Approach," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 3-28, April.
    3. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
    4. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
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