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Time-dependent limited penetrable visibility graph analysis of nonstationary time series

Author

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  • Gao, Zhong-Ke
  • Cai, Qing
  • Yang, Yu-Xuan
  • Dang, Wei-Dong

Abstract

Recent years have witnessed the development of visibility graph theory, which allows us to analyze a time series from the perspective of complex network. We in this paper develop a novel time-dependent limited penetrable visibility graph (TDLPVG). Two examples using nonstationary time series from RR intervals and gas–liquid flows are provided to demonstrate the effectiveness of our approach. The results of the first example suggest that our TDLPVG method allows characterizing the time-varying behaviors and classifying heart states of healthy, congestive heart failure and atrial fibrillation from RR interval time series. For the second example, we infer TDLPVGs from gas–liquid flow signals and interestingly find that the deviation of node degree of TDLPVGs enables to effectively uncover the time-varying dynamical flow behaviors of gas–liquid slug and bubble flow patterns. All these results render our TDLPVG method particularly powerful for characterizing the time-varying features underlying realistic complex systems from time series.

Suggested Citation

  • Gao, Zhong-Ke & Cai, Qing & Yang, Yu-Xuan & Dang, Wei-Dong, 2017. "Time-dependent limited penetrable visibility graph analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 476(C), pages 43-48.
    • Handle: RePEc:eee:phsmap:v:476:y:2017:i:c:p:43-48
    DOI: 10.1016/j.physa.2017.02.038
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    Citations

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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. Charakopoulos, A.K. & Katsouli, G.A. & Karakasidis, T.E., 2018. "Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 436-453.
    3. Hao-Ran Liu & Wei-Xing Zhou, 2023. "Visibility graph analysis of the grains and oilseeds indices," Papers 2304.05760, arXiv.org.
    4. Dai, Peng-Fei & Xiong, Xiong & Zhou, Wei-Xing, 2019. "Visibility graph analysis of economy policy uncertainty indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    5. Wang, Dong & Zhao, Yi, 2019. "Network community detection from the perspective of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 205-214.

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