Gaussian Process Regression for Forecasting Gasoline Prices in Jordan

Abstract

The purpose of this paper is to forecast monthly gasoline prices in Jordan by applying Gaussian process regression on monthly prices of two types of gasoline (octane-90 and octane-95) during the period January 2008-December 2019. Accurately predicting gasoline prices have several fiscal policy implications concerning fuel subsidies and taxes. Also, they affect the consumption and the production of decisions. Moreover, they are crucial for designing and analyzing environmental policies. The Gaussian process model was able to treat a geometric Brownian motion with a deterministic unknown drift function. The performance of prediction was measured using the Root Mean Square Error (RMSE) and the Mean Average Percentage Error (MAPE). Where the numerical results show that the model predictions of gasoline prices were accurate.