Oil price prediction using a supervised neural network

Many attempts have been made at designing efficient prediction systems for oil price due to the impact of oil on the world's economy. In this paper, we present an efficient oil price prediction system based on using a supervised neural network. The neural model receives at its input novel key economic and seasonal indicators, which we extract from the West Texas Intermediate (WTI) dataset of crude oil prices over 24 years. The model predicts at its output weekly oil prices within five dollars/barrel accuracy. The experimental results reveal that the obtained correct prediction rate of 88% is higher than rates reported in other related works, thus indicating that neural networks can be effectively used for predicting oil prices. [Received: August 3, 2016; Accepted: February 17, 2017]