Hybridizing Wavelet and Least Squares Support Vector Machines for Crude Oil Price Forecasting

In this paper, a hybrid model integrating wavelet and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, 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. And the final oil price forecasting is obtained by reconstructing the results of sub-series forecasts. The experimental results show the hybrid model, based on wavelet multi-scale decomposition, outperforms the traditional single-scale models and, furthermore, the proposed hybrid model is the best among all the models listed in this study. To fully integrate the advantages of several models, a combined forecasting model is presented. The study shows the combined forecasting may be better than that of any individual model, for crude oil price.

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