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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Crude Oil Prices Predictive Model Based on Support Vector Machine and Particle Swarm Optimization
verfasst von : Zhou Xiao-lin, Wu Hai-wei
Erschienen in: Software Engineering and Knowledge Engineering: Theory and Practice
Verlag: Springer Berlin Heidelberg
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In this paper we applied the linear kernel function , polynomial kernel function and radial basis kernel function of support vector machine, used K-fold cross-validation method, utilized the particle swarm optimization(PSO) to reach the optimum of penalty parameter C and value of gamma, and therefore built the forecast model of the crude oil price from January 2009 to December 2010.The optimized value by PSO for penalty parameter C is 10, the prediction correlation coefficient for prediction set 1 by the SVM of radial basis kernel function is 95.211%, the results are satisfactory, and we can predict the crude oil price from January to June 2011 with this model.