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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) pp 571–579Cite as

A Comparison of Multivariate Time Series Clustering Methods

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

Big Data and the IoT explosion has made clustering Multivariate Time Series (MTS) one of the most effervescent research fields. From Bio-informatics to Business and Management, MTS are becoming more and more interesting as they allow to match events the co-occur in time but that is hardly noticeable. In this paper, we compare four clustering methods retrieved from the literature analyzing their performance on five publicly available data sets. These methods make use of different TS representation and distance measurement functions. Results show that Dynamic Time Warping is still competitive; APCA+DTW and Compression-based dissimilarity obtained the best results on the different data sets.

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Acknowledgment

This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.

Author information

Authors and Affiliations

  1. Instituto Tecnológico de Castilla y León, Pol. Ind. Villalonquejar, 09001, Burgos, Spain

    Iago Vázquez & Javier Sedano

  2. Computer Science Department, University of Oviedo, Oviedo, Spain

    José Ramón Villar

  3. Department of Neurology, Clinical Centre of Vojvodina Novi Sad, University of Novi Sad, Novi Sad, Republic of Serbia

    Svetlana Simić

Authors
  1. Iago Vázquez
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  2. José Ramón Villar
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  3. Javier Sedano
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  4. Svetlana Simić
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Corresponding author

Correspondence to José Ramón Villar .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Vázquez, I., Villar, J.R., Sedano, J., Simić, S. (2021). A Comparison of Multivariate Time Series Clustering Methods. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_55

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