Abstract and Applied Analysis
Volume 2013 (2013), Article ID 528678, 7 pages
Research Article

Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine

College of Information and Control Engineering, China University of Petroleum, Qingdao, Shandong 266580, China

Received 10 December 2012; Accepted 28 January 2013

Academic Editor: Fuding Xie

Copyright © 2013 Li Shu-rong and Ge Yu-lei. 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.


A new accurate method on predicting crude oil price is presented, which is based on ε-support vector regression (ε-SVR) machine with dynamic correction factor correcting forecasting errors. We also propose the hybrid RNA genetic algorithm (HRGA) with the position displacement idea of bare bones particle swarm optimization (PSO) changing the mutation operator. The validity of the algorithm is tested by using three benchmark functions. From the comparison of the results obtained by using HRGA and standard RNA genetic algorithm (RGA), respectively, the accuracy of HRGA is much better than that of RGA. In the end, to make the forecasting result more accurate, the HRGA is applied to the optimize parameters of ε-SVR. The predicting result is very good. The method proposed in this paper can be easily used to predict crude oil price in our life.