A multi-breakpoints approach for symbolic discretization of time series


Time series discretization is a technique commonly used to tackle time series classification problems. This manuscript presents an enhanced multi-objective approach for the symbolic discretization of time series called eMODiTS. The method proposed uses a different breakpoints vector, defined per each word segment, to increase the search space of the discretization schemes. eMODiTS’ search mechanism is the well-known evolutionary multi-objective algorithm NSGA-II, which finds a set of possible solutions according to entropy, complexity, and information loss estimations. Final solutions were appraised depending on the misclassification rate computed through the decision tree classifier. The trees obtained also produce graphical and significant information from the regions, relationships, or patterns in each database. Our proposal was compared against ten state-of-the-art time symbolic discretization algorithms. The results suggest that our proposal finds a suitable discretization scheme regarding classification, dimensionality, cardinality reduction, and information loss.

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The first author acknowledges the support from the Mexican National Council for Science and Technology (CONACyT) through scholarship number 389200 to pursue graduate studies at the University of Veracruz. Besides, the first author especially acknowledges to the INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación for the borrowed computational resources and the received advice by the researchers during the research stay. The third author acknowledges support from CONACYT through project No. 220522. All the authors acknowledge to all the people who created, collected, and made available the temporal databases used in this work since without them this work would not have been successful.

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Correspondence to Aldo Márquez-Grajales.

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Márquez-Grajales, A., Acosta-Mesa, H., Mezura-Montes, E. et al. A multi-breakpoints approach for symbolic discretization of time series. Knowl Inf Syst 62, 2795–2834 (2020). https://doi.org/10.1007/s10115-020-01437-4

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  • Time series classification
  • Symbolic representation
  • Multi-breakpoints approach
  • Multi-objective optimization