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A Novel Time Series Forecasting Method Based on Fuzzy Visibility Graph

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1075))

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Abstract

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.

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Acknowledgement

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

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Authors and Affiliations

  1. School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Ministry of Education, Beijing Normal University, Beijing, 100875, China

    Jingyi Zhou, Jiayin Wang, Fusheng Yu & Lian Yu

  2. Beijing Institute of Petrochemical Technology, Beijing, China

    Xiao Wang

Authors
  1. Jingyi Zhou
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  2. Jiayin Wang
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  3. Fusheng Yu
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  4. Lian Yu
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  5. Xiao Wang
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Corresponding author

Correspondence to Jiayin Wang .

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Editors and Affiliations

  1. The University of Aizu, Aizuwakamatsu, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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Zhou, J., Wang, J., Yu, F., Yu, L., Wang, X. (2020). A Novel Time Series Forecasting Method Based on Fuzzy Visibility Graph. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_28

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