A decision trees approach to oil price prediction

In this work, an artificial intelligent approach to predicting crude oil price is presented. Decision Trees (DT) are utilized in the modeling and prediction of crude oil from a dataset covering 24 years. The input attributes to the decision tree are key economic indicators that are believed to affect crude oil price and the system has as it's output the numerical value of the predicted crude oil price. Different DT algorithms like Decision stump, Random forest, Random tree amongst others are investigated and a performance analysis is performed between the investigated algorithms. Obtained results show that DT's can be deployed with a high degree of accuracy in the prediction of crude oil price.