IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v575y2021ics0378437121003186.html
   My bibliography  Save this article

A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy

Author

Listed:
  • Wang, Xin
  • Sun, Mei

Abstract

Faced with the high-dimensional characteristics of time series, symbolization is usually used to reduce the dimensionality and effectively eliminate redundancy and irrelevant features. Several existing network prediction methods, which based on the topology of complex networks, only consider the target sequence itself, which ignored the influence of related factors. In this paper, we improve the single-layer network and construct a Multi-layer Symbolic Pattern Network (MSPN) prediction model by considering the interaction between different variables with time lag. The basic idea is to link the directed weighted networks based on causation entropy (CSE), then extract the multi-layer network topology to jointly predict the target sequence. The 2018/1–2018/12 fluctuation trend of crude oil futures prices are predicted, with auxiliary variables including crude oil supply, demand and the three major U.S. stock indexes (S&P 500 Index, NASDAQ Composite Index, and Dow Jones Industrial Average). The results show that the multi-layer network improves the directional prediction accuracy of oil price over the single-layer network, where demand and Dow Jones Industrial Average’s auxiliary perform best. It is worth noting that considering auxiliary variables can be effective in improving the accuracy of the model. The more auxiliary layers are not the better, generally considering two auxiliary layers, that is, the three-layer network prediction model can achieve higher accuracy.

Suggested Citation

  • Wang, Xin & Sun, Mei, 2021. "A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    • Handle: RePEc:eee:phsmap:v:575:y:2021:i:c:s0378437121003186
    DOI: 10.1016/j.physa.2021.126045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121003186
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126045?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Camille Logeay & Sven Schreiber, 2006. "Testing the effectiveness of the French work-sharing reform: a forecasting approach," Applied Economics, Taylor & Francis Journals, vol. 38(17), pages 2053-2068.
    2. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," Economic Modelling, Elsevier, vol. 32(C), pages 15-22.
    3. Islam, Faridul & Shahbaz, Muhammad & Ahmed, Ashraf U. & Alam, Md. Mahmudul, 2013. "Financial development and energy consumption nexus in Malaysia: A multivariate time series analysis," Economic Modelling, Elsevier, vol. 30(C), pages 435-441.
    4. Wang, Qingfeng & Sun, Xu, 2017. "Crude oil price: Demand, supply, economic activity, economic policy uncertainty and wars – From the perspective of structural equation modelling (SEM)," Energy, Elsevier, vol. 133(C), pages 483-490.
    5. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    6. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.
    7. Stavros Degiannakis, George Filis, and Renatas Kizys, 2014. "The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    8. Brida, Juan G. & Punzo, Lionello F., 2003. "Symbolic time series analysis and dynamic regimes," Structural Change and Economic Dynamics, Elsevier, vol. 14(2), pages 159-183, June.
    9. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    10. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    11. Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
    12. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    13. Yudong Wang & Li Liu, 2016. "Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging," Empirical Economics, Springer, vol. 50(4), pages 1481-1509, June.
    14. Broadstock, David C. & Filis, George, 2014. "Oil price shocks and stock market returns: New evidence from the United States and China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 417-433.
    15. Wang, Minggang & Tian, Lixin, 2016. "From time series to complex networks: The phase space coarse graining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 456-468.
    16. Xiao, Yunpeng & Xie, Xiaoqiu & Li, Qian & Li, Tun, 2019. "Nonlinear dynamics model for social popularity prediction based on multivariate chaotic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1259-1275.
    17. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    18. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    19. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    20. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Lin, Guancen & Lin, Aijing, 2022. "Modified multiscale sample entropy and cross-sample entropy based on horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    3. Guo, Jingjun & Zhao, Zhengling & Sun, Jingyun & Sun, Shaolong, 2022. "Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework," Resources Policy, Elsevier, vol. 77(C).
    4. Lin, Guancen & Lin, Aijing & Mi, Yujia & Gu, Danlei, 2023. "Measurement of information transfer based on phase increment transfer entropy," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
    4. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    5. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    6. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & de Gracia, Fernando Perez, 2020. "Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness," Energy Economics, Elsevier, vol. 91(C).
    7. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    8. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    9. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
    10. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    11. Stavros Degiannakis, George Filis, and Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    12. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Spillovers between oil and stock markets at times of geopolitical unrest and economic turbulence," MPRA Paper 59760, University Library of Munich, Germany.
    13. Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.
    14. Rehman, Mobeen Ur, 2019. "Energy shocks pricing model: A non-linear US sectoral based analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    15. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & de Gracia, Fernando Perez, 2023. "Dynamic connectedness among the implied volatilities of oil prices and financial assets: New evidence of the COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 114-123.
    16. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    17. Li, Lei & Yin, Libo & Zhou, Yimin, 2016. "Exogenous shocks and the spillover effects between uncertainty and oil price," Energy Economics, Elsevier, vol. 54(C), pages 224-234.
    18. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
    19. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    20. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2017. "Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 1-26.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:575:y:2021:i:c:s0378437121003186. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.