AN INTEGRATED MODEL USING WAVELET DECOMPOSITION AND LEAST SQUARES SUPPORT VECTOR MACHINES FOR MONTHLY CRUDE OIL PRICES FORECASTING

Yejing Bao (), Xun Zhang (), Lean Yu (), Kin Keung Lai () and Shouyang Wang ()
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Yejing Bao: Department of Economics and Management, College of Pilot, Beijing University of Technology, Beijing 101101, China;
Xun Zhang: 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

New Mathematics and Natural Computation (NMNC), 2011, vol. 07, issue 02, 299-311

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.

Keywords: Crude oil price forecasting; Haar à trous wavelet transform; least squares support vector machines; hybrid model (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (5)

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DOI: 10.1142/S1793005711001949

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