Forecasting crude oil price with ensemble neural networks based on different feature subsets method

In this study, an ensemble neural network is proposed based on different feature subsets method in order to forecast the world crude oil spot price. To this end, a number of experts in database gathering and appropriate time delays were interviewed to forecast 1-step ahead of the crude oil spot price. Subsequently, different features subsets were generated randomly, each of which was then used for each of the basic classifiers. Then, three-layered feed-forward neural network models were used to model each of the basic classifiers. Finally, the prediction results of all basic classifiers were combined with a single layer perceptron neural network to formulate an ensemble output for the original crude oil price series. In order to verify and evaluate the presented method, one of the main crude oil price series, i.e. WTI crude oil spot price, was used to test the effectiveness of the proposed method. Empirical results provided evidence for the effectiveness of the proposed ensemble learning method compared to linear and nonlinear models.