Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price

In this paper, hybrid methods are proposed to predict OPEC crude oil. In the pre-processing step, the wavelet decomposition has been used to reduce the noise of time series, which divides the original data into five levels. Also, the fuzzy transform (F-transform) is applied for its potency of management uncertainty due to the fluctuation of data. F-transform is for decomposing the time series data and afterward, this decomposed data are employed on the ground of inputs. Because of the nonlinearity of the time series structure, integrating the mentioned proposed algorithms with the multilayer perceptron (MLP), radial basis functions, group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) are applied for prediction of time series relevant to the crude oil price. By comparing the results of models, L5-ANFIS, L5-MLP, L5-GMDH, L5-SVR are more appropriate than others for predicting OPEC crude oil price.