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Visibility Graph Based Time Series Analysis

Author

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  • Mutua Stephen
  • Changgui Gu
  • Huijie Yang

Abstract

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Suggested Citation

  • Mutua Stephen & Changgui Gu & Huijie Yang, 2015. "Visibility Graph Based Time Series Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
  • Handle: RePEc:plo:pone00:0143015
    DOI: 10.1371/journal.pone.0143015
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    References listed on IDEAS

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    Cited by:

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    2. Liu, Hongzhi & Zhang, Xingchen & Zhang, Xie, 2018. "Exploring dynamic evolution and fluctuation characteristics of air traffic flow volume time series: A single waypoint case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 560-571.
    3. 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.
    4. Sangho Lee & Jeongsub Choi & Youngdoo Son, 2024. "Efficient visibility algorithm for high-frequency time-series: application to fault diagnosis with graph convolutional network," Annals of Operations Research, Springer, vol. 339(1), pages 813-833, August.
    5. Yuan, Qianshun & Semba, Sherehe & Zhang, Jing & Weng, Tongfeng & Gu, Changgui & Yang, Huijie, 2021. "Multi-scale transition matrix approach to time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    6. Yue Yang & Changgui Gu & Qin Xiao & Huijie Yang, 2017. "Evolution of scaling behaviors embedded in sentence series from A Story of the Stone," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-14, February.
    7. Yuan, Qianshun & Zhang, Jing & Wang, Haiying & Gu, Changgui & Yang, Huijie, 2023. "A multi-scale transition matrix approach to chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    8. Dong-Rui Chen & Chuang Liu & Yi-Cheng Zhang & Zi-Ke Zhang, 2019. "Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices," Complexity, Hindawi, vol. 2019, pages 1-17, October.
    9. Yan, Shuang & Gu, Changgui & Yang, Huijie, 2024. "Bridge successive states for a complex system with evolutionary matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    10. Chen, Yu & Ling, Guang & Song, Xiangxiang & Tu, Wenhui, 2023. "Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    11. Jamshid Ardalankia & Jafar Askari & Somaye Sheykhali & Emmanuel Haven & G. Reza Jafari, 2020. "Mapping Coupled Time-series Onto Complex Network," Papers 2004.13536, arXiv.org, revised Aug 2020.
    12. Saldivia, Sebastián & Pastén, Denisse & Moya, Pablo S., 2024. "Using visibility graphs to characterize non-Maxwellian turbulent plasmas," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).

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