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Applying Visibility Graphs to Classify Time Series

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 77))

Abstract

In the work, a study of the characteristics of natural visibility graphs was carried out. The data for constructing the graphs were model chaotic realizations and realizations of RR intervals in different cardiac diseases. For visibility graphs, adjacency matrices were built, on the ba-sis of which the following characteristics were calculated: density, integral clustering coefficient, integral centrality, average eccentricity, etc. The results showed that differences in quantitative characteristics of graphs for different types of time series are significant. Quantitative characteristics of visibility graphs can be used as features for classify-ing time series using machine learning methods.

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Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, Kharkiv, 61166, Ukraine

    Lyudmyla Kirichenko, Tamara Radivilova & Vitalii Ryzhanov

Authors
  1. Lyudmyla Kirichenko
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  2. Tamara Radivilova
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  3. Vitalii Ryzhanov
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Corresponding author

Correspondence to Lyudmyla Kirichenko .

Editor information

Editors and Affiliations

  1. Department of Physics, Kherson State University, Kherson, Ukraine

    Sergii Babichev

  2. Department of Informatics and Computer Science, Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Kirichenko, L., Radivilova, T., Ryzhanov, V. (2022). Applying Visibility Graphs to Classify Time Series. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_26

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