Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 959040, 12 pages
Research Article

Applying Neural Networks to Prices Prediction of Crude Oil Futures

1Department of Finance, Chung Yuan Christian University, Chung-Li 32023, Taiwan
2Department of Business Administration, Chung Yuan Christian University, Chung-Li 32023, Taiwan
3Tsann Kuen Trans-Nation Group, Taiwan

Received 2 March 2012; Accepted 12 June 2012

Academic Editor: Jung-Fa Tsai

Copyright © 2012 John Wei-Shan Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The global economy experienced turbulent uneasiness for the past five years owing to large increases in oil prices and terrorist’s attacks. While accurate prediction of oil price is important but extremely difficult, this study attempts to accurately forecast prices of crude oil futures by adopting three popular neural networks methods including the multilayer perceptron, the Elman recurrent neural network (ERNN), and recurrent fuzzy neural network (RFNN). Experimental results indicate that the use of neural networks to forecast the crude oil futures prices is appropriate and consistent learning is achieved by employing different training times. Our results further demonstrate that, in most situations, learning performance can be improved by increasing the training time. Moreover, the RFNN has the best predictive power and the MLP has the worst one among the three underlying neural networks. This finding shows that, under ERNNs and RFNNs, the predictive power improves when increasing the training time. The exceptional case involved BPNs, suggesting that the predictive power improves when reducing the training time. To sum up, we conclude that the RFNN outperformed the other two neural networks in forecasting crude oil futures prices.