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An efficient forecasting method for time series based on visibility graph and multi-subgraph similarity

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  • Hu, Yuntong
  • Xiao, Fuyuan

Abstract

Recently network-based method for forecasting time series has become a hot research topic. Although some methods have been recognized for their prediction performance, how to mine more useful information of time series and make accuracy predictions is still an open question. To address this issue, we first propose a novel similarity measure called multi-subgraph similarity (Mss) for nodes in visibility graph. Then, a novel well-performed forecasting method for time series is proposed based on Mss. First, a time series is converted into a visibility graph. Afterward, the similarity distribution is obtained by Mss. Eventually, the prediction of time series is made using the similarity distribution. To demonstrate the proposed method is of better prediction performance, we compare the results of forecasting Construction Cost Index (CCI) and UCR data sets. The experiment results indicate that the proposed method could provide more accuracy predictions than compared methods. Moreover, the robustness test shows that the proposed method is of good robustness.

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  • Hu, Yuntong & Xiao, Fuyuan, 2022. "An efficient forecasting method for time series based on visibility graph and multi-subgraph similarity," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
  • Handle: RePEc:eee:chsofr:v:160:y:2022:i:c:s0960077922004532
    DOI: 10.1016/j.chaos.2022.112243
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    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    3. Qiuya Gao & Tao Wen & Yong Deng, 2021. "Information Volume Fractal Dimension," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 29(08), pages 1-9, December.
    4. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    5. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    6. Zhan, Xiu-Xiu & Liu, Chuang & Zhou, Ge & Zhang, Zi-Ke & Sun, Gui-Quan & Zhu, Jonathan J.H. & Jin, Zhen, 2018. "Coupling dynamics of epidemic spreading and information diffusion on complex networks," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 437-448.
    7. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    8. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    9. Elvin Isufi & Andreas Loukas & Nathanael Perraudin & Geert Leus, 2018. "Forecasting Time Series with VARMA Recursions on Graphs," Papers 1810.08581, arXiv.org, revised Jul 2019.
    10. Niklas Boers & Bedartha Goswami & Aljoscha Rheinwalt & Bodo Bookhagen & Brian Hoskins & Jürgen Kurths, 2019. "Complex networks reveal global pattern of extreme-rainfall teleconnections," Nature, Nature, vol. 566(7744), pages 373-377, February.
    11. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    12. Rong Zhang & Baabak Ashuri & Yong Deng, 2017. "A novel method for forecasting time series based on fuzzy logic and visibility graph," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 759-783, December.
    13. Hajirahimi, Zahra & Khashei, Mehdi & Etemadi, Sepideh, 2022. "A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
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    Cited by:

    1. Schmidt, Jonas & Köhne, Daniel, 2023. "A simple scalable linear time algorithm for horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).

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