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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)

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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.

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Bibliographic Info

Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal New Mathematics and Natural Computation.

Volume (Year): 07 (2011)
Issue (Month): 02 ()
Pages: 299-311

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Handle: RePEc:wsi:nmncxx:v:07:y:2011:i:02:p:299-311

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Keywords: Crude oil price forecasting; Haar à trous wavelet transform; least squares support vector machines; hybrid model;

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