Backpropagation neural networks for modeling gasoline consumption

Abstract

This paper presents an artificial neural network (ANN) approach to gasoline consumption (GC) forecasting in Lebanon. In order to provide the forecasted gasoline consumption, the ANN interpolates among the GC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real GC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on GC time series and price (P). The third model is also a multivariate model based on GC and car registration (CR). Finally, the fourth model combines GC, P and CR. Forecasting performance measures, such as mean square errors and mean absolute deviations, are presented for all models.