基于SVR的混沌时间序列预测

Chaotic Time Series Prediction Based on SVR

支持向量机是一种基于统计学习理论的新颖的机器学习方法,由于其出色的学习性能,该技术已成为当前国际机器学习界的研究热点。这种方法已广泛用于解决分类和回归问题。论文介绍了支持向量回归算法的各种版本,同时将它们应用到混沌时间序列预测中,并且比较了它们的预测性能,为实际应用合理选择模型提供一定的依据。

Support vector machines(SVM)are a kind of novel machine learning methods,based on statistical learning theory,which have become the hotspot of machine learning because of their excellent learning performance.The method of support vector machines has been developed for solving classification and regression problems.Several versions of support vector regression(SVR)are introduced in this paper,and then apply them to chaotic time series prediction.Their performance of prediction is analyzed,which can provide some foundation about reasonable selecting models in practice.