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An Integrated Model Using Wavelet Decomposition And Least Squares Support Vector Machines For Monthly Crude Oil Prices Forecasting

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  • YEJING BAO

    (Department of Economics and Management, College of Pilot, Beijing University of Technology, Beijing 101101, China;
    Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • XUN ZHANG

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • LEAN YU

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • KIN KEUNG LAI

    (Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong)

  • SHOUYANG WANG

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

In this paper, a hybrid model integrating wavelet decomposition and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, the Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. Subsequently, the final oil price forecast is obtained by reconstructing the results of the sub-series forecasts. The experimental results show that the integrated model, based on multi-scale wavelet decomposition, outperforms the traditional single-scale models. Furthermore, the proposed hybrid model is the best among all the models compared in this study. To fully integrate the advantages of several models, a combined forecasting model is presented. The study shows that the combined forecasting model is clearly better than any individual model for crude oil price forecasting.

Suggested Citation

  • Yejing Bao & Xun Zhang & Lean Yu & Kin Keung Lai & Shouyang Wang, 2011. "An Integrated Model Using Wavelet Decomposition And Least Squares Support Vector Machines For Monthly Crude Oil Prices Forecasting," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 299-311.
    • Handle: RePEc:wsi:nmncxx:v:07:y:2011:i:02:n:s1793005711001949
    DOI: 10.1142/S1793005711001949
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    Citations

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    Cited by:

    1. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    2. Jiang Wu & Feng Miu & Taiyong Li, 2020. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market," Energies, MDPI, vol. 13(7), pages 1-20, April.
    3. 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).
    4. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    5. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.

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