Oil barrel price forecasting: A case study of Saudi Arabia

In this study, various models are used to forecast oil barrel price (OBP) in Saudi Arabia using artificial neural networks (ANNs). The proposed models are based on a multilayer feedforward neural network with backpropagation (MFFNNBP). A number of features were used as inputs to the neural network, such as gross domestic product (GDP), inflation rate, and unemployment rate. The results show that, with meticulous design and appropriate training inputs, MFFNNBP models can predict OBP with a low margin of error.