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The parametric modified limited penetrable visibility graph for constructing complex networks from time series

Author

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  • Li, Xiuming
  • Sun, Mei
  • Gao, Cuixia
  • Han, Dun
  • Wang, Minggang

Abstract

This paper presents the parametric modified limited penetrable visibility graph (PMLPVG) algorithm for constructing complex networks from time series. We modify the penetrable visibility criterion of limited penetrable visibility graph (LPVG) in order to improve the rationality of the original penetrable visibility and preserve the dynamic characteristics of the time series. The addition of view angle provides a new approach to characterize the dynamic structure of the time series that is invisible in the previous algorithm. The reliability of the PMLPVG algorithm is verified by applying it to three types of artificial data as well as the actual data of natural gas prices in different regions. The empirical results indicate that PMLPVG algorithm can distinguish the different time series from each other. Meanwhile, the analysis results of natural gas prices data using PMLPVG are consistent with the detrended fluctuation analysis (DFA). The results imply that the PMLPVG algorithm may be a reasonable and significant tool for identifying various time series in different fields.

Suggested Citation

  • Li, Xiuming & Sun, Mei & Gao, Cuixia & Han, Dun & Wang, Minggang, 2018. "The parametric modified limited penetrable visibility graph for constructing complex networks from time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1097-1106.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1097-1106
    DOI: 10.1016/j.physa.2017.11.040
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    Cited by:

    1. Yu, Xuan & Shi, Suixiang & Xu, Lingyu & Yu, Jie & Liu, Yaya, 2020. "Analyzing dynamic association of multivariate time series based on method of directed limited penetrable visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Wang, Minggang & Hua, Chenyu & Zhu, Mengrui & Xie, Shangshan & Xu, Hua & Vilela, André L.M. & Tian, Lixin, 2022. "Interrelation measurement based on the multi-layer limited penetrable horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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