Modeling and forecasting opec reference basket crude oil prices using artificial neural networks

Abstract

With Organization of petroleum exporting countries (OPEC) being the dominant player in the crude oil industry, it is of great importance to accurately forecast OPEC reference basket (ORB) crude oil prices. This study focuses on forecasting ORB crude oil prices using two Artificial Neural Network (ANN) models: Feedforward Neural Network (FFNN) and Time-Delay Neural Network (TDNN). Training of the networks were done by changing several parameters of the networks. Levenberg-Marquardt training algorithm has been used in training ANN. Results indicates that the TDNN model outperforms the FFNN model in forecasting daily ORB crude oil prices.