A hybrid denoising and artificial neural network approach for diesel fuel price prediction

Diesel fuel price (DFP) modeling and prediction are important to an economy since fuel price has direct consequences on retail commodity prices, transportation, and the successful implementation of government policies. In this study, an attempt has been made to propose a DFP prediction model using the wavelet transform backpropagation neural network (WTBPNN) approach. The proposed WTBPNN approach was compared with the following benchmark methods: BPNN, radial basis function neural network (RBFNN), and wavelet transform radial basis function neural network (WTRBFNN). The developed prediction models used interest and inflation rates as the input parameters and DFP as the output parameter. A total of 95 data points obtained from the Ghana National Petroleum Authority and the Bank of Ghana were considered. Thus, 67 served as the training set and 28 were used as the testing set. Model validation was performed using dimensioned error statistic indicators of mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of determination (R2), and Pearson’s product-moment correlation coefficient (R). Overall, the statistical results revealed that the proposed WTBPNN approach gave the best performance and thus could be used to predict DFP.