期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：72
期号：2
出版社：Journal of Theoretical and Applied
摘要：In the recent years, the crude oil is one of the most important commodities worldwide. This paper discusses the prediction of crude oil using artificial neural networks techniques. The research data used in this study is from 1st Jan 2000- 31st April 2014. Normally, Crude oil is related with other commodities. Hence, in this study, the commodities like historical data�s of gold prices, Standard & Poor�s 500 stock index (S & P 500) index and foreign exchange rate are considered as inputs for the network. A radial basis function is better than the back propagation network in terms of classification and learning speed. When creating a radial basis functions, the factors like number of radial basis neurons, radial layer�s spread constant are taken into an account. The spread constant is determined using a bio inspired particle swarm optimization algorithm. A hybrid Radial Basis Function is proposed for forecasting the crude oil prices. The accuracy measures like Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error are used to access the performance. From the results, it is clear that hybrid radial basis function outperforms the other models.
