Crude oil price prediction based on multi-scale decomposition

Abstract

A synergetic model (DWT-LSSVM) is presented in this paper. First of all, the raw data is decomposed into approximate coefficients and the detail coefficients at different scales by discrete wavelet transforms (DWT). These coefficients obtained by previous phase are then used for prediction independently using least squares support vector machines (LSSVM). Finally, these predicted coefficients are combined into a final prediction. The proposed model is applied to oil price prediction. The simulation results show that the synergetic model has greater generalization ability and higher accuracy. © Springer-Verlag Berlin Heidelberg 2007.