Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study

Abstract

Recently, the petroleum sector in Morocco has been liberalized which has a significant effect for petroleum product distributors. Since the beginning of December 2015, fuel prices are freely determined. This event presents many constraints affecting the balance of the sector plus the competition between its economic players. The lack of accompanying measures by the State makes this vital reform for public finances that stop subsidizing the price of gasoline vulnerable. As all fuel products are imported, we will be interested in the evolution by making forecasts of the price of fuels in the Moroccan market. In this context, our paper aims mainly to study the selling price of diesel and gasoline in order to provide precise forecasts to the company and to respect the permissible error margin of 3%. To this end, we worked with a widely used approach for price forecasting: artificial neural networks technique (radial basis function). Recently, it is suggested to work with artificial neural networks in forecasting field as an alternative to the traditional linear methods. We developed a radial basis function network to come up with conclusions in terms of the superiority in forecasting performance. Consequently, the radial basis function technique proved its strength manifested in the error that was further minimized: 1.95% instead of 2.85% for autoregressive integrated moving average (ARIMA) model used in our previous work. The error is further minimized by applying radial basis function technique.

Seeking Accuracy in Forecasting Demand and Selling Prices: Comparison of Various Methods

“…The radial basis ANN model (comprising two layers) is trained for implementing the back propagation algorithm to minimize the mean squared error with one parameter (time) as the input and the desired output (fuel selling price). As presented on the visualization of the network shown in Figure 12, the first layer has radial basis transfer functions with the maximum number of 80 neurons, and the second layer has a linear transfer function, in order to build a consistent model for providing accurate forecasts [27].…”

“…Besides, in our study, we implemented radial basis network of the MATLAB toolbox (i.e., "nwrb"). Furthermore, the Gaussian function is the main kernel function implemented here with the width parameter of 1 [27].…”

“…In the next step, Forecasting in Mathematics -Recent Advances, New Perspectives and Applications we will consider 80% of the total dataset for the training phase and 20% for testing the model. Table 8 summarizes the different combinations [27].…”