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Time series characterization via horizontal visibility graph and Information Theory

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  • Gonçalves, Bruna Amin
  • Carpi, Laura
  • Rosso, Osvaldo A.
  • Ravetti, Martín G.

Abstract

Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a popular method due to its simplicity and good results when applied to natural and artificially generated data. In this work, we explore different ways of extracting information from the network constructed from the horizontal visibility graph and evaluated by Information Theory quantifiers. Most works use the degree distribution of the network, however, we found alternative probability distributions, more efficient than the degree distribution in characterizing dynamical systems. In particular, we find that, when using distributions based on distances and amplitude values, significant shorter time series are required. We analyze fractional Brownian motion time series, and a paleoclimatic proxy record of ENSO from the Pallcacocha Lake to study dynamical changes during the Holocene.

Suggested Citation

  • Gonçalves, Bruna Amin & Carpi, Laura & Rosso, Osvaldo A. & Ravetti, Martín G., 2016. "Time series characterization via horizontal visibility graph and Information Theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 93-102.
    • Handle: RePEc:eee:phsmap:v:464:y:2016:i:c:p:93-102
    DOI: 10.1016/j.physa.2016.07.063
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    References listed on IDEAS

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    Cited by:

    1. Spichak, David & Kupetsky, Audrey & Aragoneses, Andrés, 2021. "Characterizing complexity of non-invertible chaotic maps in the Shannon–Fisher information plane with ordinal patterns," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Borges, João B. & Ramos, Heitor S. & Mini, Raquel A.F. & Rosso, Osvaldo A. & Frery, Alejandro C. & Loureiro, Antonio A.F., 2019. "Learning and distinguishing time series dynamics via ordinal patterns transition graphs," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    3. Li, Sange & Shang, Pengjian, 2021. "Analysis of nonlinear time series using discrete generalized past entropy based on amplitude difference distribution of horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    4. Gao, Meng & Ge, Ruijun, 2024. "Mapping time series into signed networks via horizontal visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

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