Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study. (October 2019)

• Record Type: Journal Article
• Authors: Aman, Zineb; Ezzine, Latifa; El Bahi, Younes Fakhradine; EL Moussami, Haj
• Is Part Of: Journal of algorithms & computational technology. Volume 13(2019)
• Publication Date: 2019-10
• Subjects: Liberalization of the petroleum sector -- forecast -- artificial neural networks (radial basis function); Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302619881120
• Languages: English
• ISSNs: 1748-3018

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.