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Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network

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  • Huang, Lili
  • Wang, Jun

Abstract

In the present paper, a new neural network is developed to improve the prediction accuracy of crude oil price fluctuations. The proposed model combines wavelet neural network (WNN) with random time effective function. WNN is a predictive system with the ability to implement strong nonlinear approximation. The random time effective function is applied to formulate the varied impact of historical data on current market, which endows historical data with time-variant weights to make them affect differently on the training process of WNN. Besides, the multiscale composite complexity synchronization (MCCS) is used as the new method to evaluate the predictive performance. The empirical experiments are implemented in predicting crude oil prices and moving average absolute return series of WTI and BRE. Through comparing with the traditional back propagation neural network (BPNN), support vector machine (SVM) and WNN models, the empirical results demonstrate that the proposed model has a higher accuracy in crude oil price fluctuations predicting and is advantageous in improving the precision of prediction.

Suggested Citation

  • Huang, Lili & Wang, Jun, 2018. "Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network," Energy, Elsevier, vol. 151(C), pages 875-888.
    • Handle: RePEc:eee:energy:v:151:y:2018:i:c:p:875-888
    DOI: 10.1016/j.energy.2018.03.099
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    8. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
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    14. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
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    17. 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.
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    19. Adam Kula & Albert Smalcerz & Maciej Sajkowski & Zygmunt Kamiński, 2021. "Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions," Energies, MDPI, vol. 14(22), pages 1-20, November.
    20. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    21. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    22. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    23. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
    24. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).

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