Development of a New Numerical Model of Dynamic Harmonic Regression for the Forecast of Selling Fuel Price in the Moroccan Petroleum Sector

Abstract

The liberalization of the petroleum sector in Morocco 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 among its economic players. 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 time series 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, the harmonic dynamic regression model through the proposed process approach yielded excellent forecasting results for the first quarter of 2017 with an average error margin of 1.617%. Compared to ARIMA model, the harmonic dynamic regression proves its strength manifested in the low rate of error. In addition, the assumption that the residuals are a Gaussian white noise has always been verified. The forecasts obtained are very crucial for managers to take good decisions at the strategic level.