Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand

G. E. Nasr, E. A. Badr, and C. Joun

This paper applies artificial neural networks to forecast gasoline consumption. The ANN is implemented using the cross entropy error function in the training stage. The cross entropy function is proven to accelerate the backpropagation algorithm and to provide good overall network performance with relatively short stagnation periods. To forecast gasoline consumption (GC), the ANN uses previous GC data and its determinants in a training data set. The determinants of gasoline consumption employed in this study are the price (P) and car registration (CR). Two ANNs models are presented. The first model is a univariate model based on past GC values. The second model is a trivariate model based on GC, price and car registration time series. Forecasting performance measures such as mean square errors (MSE) and mean absolute deviations (MAD) are presented for both models.


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