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Visibility graph analysis of wind speed records measured in central Argentina

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  • Pierini, Jorge O.
  • Lovallo, Michele
  • Telesca, Luciano

Abstract

Hourly means of wind speed time series recorded at two wind stations in central Argentina (one inland and the other coastal) are analyzed by means of the visibility graph method. The degree distribution of both series was calculated along with that of their shuffled, Gaussian- and uniform-distributed surrogates. The original series as well as their surrogates show an apparent exponential degree distribution. The v–k (value–degree) plot of the original wind series indicates that the higher values of the series are not necessarily “hubs” for the series, while that of the surrogates are characterized by a reduced connectivity degree of the higher hubs.

Suggested Citation

  • Pierini, Jorge O. & Lovallo, Michele & Telesca, Luciano, 2012. "Visibility graph analysis of wind speed records measured in central Argentina," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 5041-5048.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:20:p:5041-5048
    DOI: 10.1016/j.physa.2012.05.049
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    References listed on IDEAS

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    1. Kavasseri, Rajesh G. & Nagarajan, Radhakrishnan, 2005. "A multifractal description of wind speed records," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 165-173.
    2. 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.
    3. Rehman, Shafiqur & Halawani, Talal Omar, 1994. "Statistical characteristics of wind in Saudi Arabia," Renewable Energy, Elsevier, vol. 4(8), pages 949-956.
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    Cited by:

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    2. Lahmiri, Salim, 2015. "Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 130-138.
    3. B. Zhang & J. Wang & W. Zhang & G. C. Wang, 2020. "Nonlinear Scaling Behavior of Visible Volatility Duration for Financial Statistical Physics Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 373-389, August.
    4. Wu, Xuehui & Wu, Zhong & Hu, Jun, 2022. "Global competitiveness analysis of industrial robot technology innovations market layout using visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Telesca, Luciano & Lovallo, Michele & Toth, Laszlo, 2014. "Visibility graph analysis of 2002–2011 Pannonian seismicity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 219-224.
    6. Stosic, Tatijana & Telesca, Luciano & Stosic, Borko, 2021. "Multiparametric statistical and dynamical analysis of angular high-frequency wind speed time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    7. Zhang, Bo & Wang, Jun & Fang, Wen, 2015. "Volatility behavior of visibility graph EMD financial time series from Ising interacting system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 301-314.

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    Keywords

    Wind; Visibility graph;

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