forecasting crude oil prices: a hybrid model based on wavelet transforms and neural networks

in general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. it is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. one could argue that these random changes act like noise which effects the deterministic variations in prices. in this paper, we employ the wavelet transform as a tool for smoothing and minimizing the noise presented in crude oil prices, and then investigate the effect of wavelet smoothing on oil price forecasting while using the gmdh neural network as the forecasting model. furthermore, the generalized auto-regressive conditional hetroscedasticity model is used for capturing time varying variance of crude oil price. in order to evaluate the proposed hybrid model, we employ crude oil spot price of new york and los angles markets. results reveal that the prediction performance improves by more than 40% when the effect of noise is minimized and variance is captured by auto-regressive conditional hetroscedasticity model.