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Phase space visibility graph

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  • Ren, Weikai
  • Jin, Zhijun

Abstract

We introduce a topological approach for quantifying the dynamical complexity of time series. A novel complex network of visibility graph family is proposed based on defining visibility algorithm in phase space. The statistical properties of the constructed network show powerful potentiality for distinguishing stochastic and chaotic systems. And for some remarkable chaotic systems, it allows for quantitative correspondence with Lyapunov exponents. The potential practical application of this approach is demonstrated on the multiphase flow system and bearing fault identification. This work paves a natural way constructing complex network in phase space, and provides a complexity quantitative estimate for chaotic time series.

Suggested Citation

  • Ren, Weikai & Jin, Zhijun, 2023. "Phase space visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s096007792301072x
    DOI: 10.1016/j.chaos.2023.114170
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    References listed on IDEAS

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    1. Andriana S L O Campanharo & M Irmak Sirer & R Dean Malmgren & Fernando M Ramos & Luís A Nunes Amaral, 2011. "Duality between Time Series and Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-13, August.
    2. Nepomuceno, Erivelton Geraldo & Mendes, Eduardo M.A.M., 2017. "On the analysis of pseudo-orbits of continuous chaotic nonlinear systems simulated using discretization schemes in a digital computer," Chaos, Solitons & Fractals, Elsevier, vol. 95(C), pages 21-32.
    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).
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