A Novel Time Series Forecasting Method Based on Fuzzy Visibility Graph

  • Jingyi Zhou
  • Jiayin WangEmail author
  • Fusheng Yu
  • Lian Yu
  • Xiao Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


Aiming at the defect of visibility graph, this paper first proposes the definition of fuzzy visibility graph, then gives a new similarity measure of time series induced from the similarity of fuzzy visibility graphs. Based on the proposed definition and similarity measure, a novel time series forecasting method is established. To demonstrate the performance of the proposed method, experiments are carried out on Alabama enrollment, stock price index and Shanghai Pudong Development Bank’s closing price. The results show that the proposed method improves the accuracy of prediction.


Forecasting Time series Visibility graph Fuzzy visibility graph 



This work is supported by the National Natural Science Foundation of China (No. 11571001, No. 11701338).


  1. 1.
    Tiwari, A.K., Suresh, K.G., Arouri, M., Teulon, F.: Causality between consumer price and producer price: evidence from Mexico. Econ. Model. 36, 432–440 (2014)CrossRefGoogle Scholar
  2. 2.
    Wang, D., Podobnik, B., Horvatić, D., Stanley, H.E.: Quantifying and modeling long-range cross correlations in multiple time series with applications to world stock indices. Phys. Rev. E 83(4), 046121 (2011)CrossRefGoogle Scholar
  3. 3.
    Brown, R.G.: Exponential Smoothing for Predicting Demand. Operations Research, vol. 5, p. 145. Institute for Operations Research and the Management Sciences, Linthicum (1957)Google Scholar
  4. 4.
    Box, G., Jenjins, G.: Time Series Analysis, Forecasting and Control. Holden-Day (1970)Google Scholar
  5. 5.
    Brown, R.G.: Statistical forecasting for inventory control. J. Roy. Stat. Soc. 123(3) (1959)Google Scholar
  6. 6.
    Maguire, L.P., Roche, B., Mcginnity, T.M., et al.: Predicting chaotic time series using a fuzzy neural network. Inf. Sci. 112, 125–136 (1998)zbMATHCrossRefGoogle Scholar
  7. 7.
    Casdagli, M.: Nonlinear prediction chaotic time series. Phys. D 35, 335–356 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series. Part I. Fuzzy Sets Syst. 54(1), 1–9 (1993)CrossRefGoogle Scholar
  9. 9.
    Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series. Part II. Fuzzy Sets Syst. 62(1), 1–8 (1994)CrossRefGoogle Scholar
  10. 10.
    Jilani, T.A., Burney, S.M.A.: A refined fuzzy time series model for stock market forecasting. Phys. A 387(12), 2857–2862 (2008)CrossRefGoogle Scholar
  11. 11.
    Zhang, H., Wei, D., Hu, Y., Lan, X., Deng, Y.: Modeling the self-similarity in complex networks based on Coulombs law. Commun. Nonlinear Sci. Numer. Simul. 35, 97–104 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wang, S., Du, Y., Deng, Y.: A new measure of identifying influential nodes: efficiency centrality. Commun. Nonlinear Sci. Numer. Simul. 47, 151–163 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lacasa, L., Luque, B., Ballesteros, F., et al.: From time series to complex networks: the visibility graph. Proc. Natl. Acad. Sci. U.S.A. 105(13), 4972–4975 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Zhang, R., Ashuri, B., et al.: Forecasting construction cost index based on visibility graph: a network approach. Phys. A Stat. Mech. Appl. 493, 239–252 (2017)CrossRefGoogle Scholar
  15. 15.
    Wang, M., Vilela, A.L., Tian, L., et al.: A new time series prediction method based on complex network theory. In: 2017 IEEE International Conference on Big Data, pp. 4170–4175. IEEE (2018)Google Scholar
  16. 16.
    Zhang, R., Ashuri, B., Deng, Y.: A novel method for forecasting time series based on fuzzy logic and visibility graph. Adv. Data Anal. Classif. 11(4), 759–783 (2017)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)zbMATHCrossRefGoogle Scholar
  18. 18.
    Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80(4), 046103 (2009)CrossRefGoogle Scholar
  19. 19.
    Zhou, T.T., Jin, N.D., Gao, Z.K., Luo, Y.B.: Limited penetrable visibility graph for establishing complex network from time series. Acta Phys. Sin. 61(3), 030506 (2012)Google Scholar
  20. 20.
    Bezsudnov, I.V., Snarskii, A.A.: From the time series to the complex networks: the parametric natural visibility graph. Phys. A 414, 53–60 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Li, X., Sun, M., Gao, C., et al.: The parametric modified limited penetrable visibility graph for constructing complex networks from time series. Phys. A 492, 1097–1106 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jingyi Zhou
    • 1
  • Jiayin Wang
    • 1
    Email author
  • Fusheng Yu
    • 1
  • Lian Yu
    • 1
  • Xiao Wang
    • 2
  1. 1.School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Ministry of EducationBeijing Normal UniversityBeijingChina
  2. 2.Beijing Institute of Petrochemical TechnologyBeijingChina

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