基于遗传算法的支持向量机时间序列预测模型优化

建立在统计学习理论和结构风险最小原则上的支持向量机在理论上保证了模型的最大泛化能力，因此与建立在经验风险最小原则上的神经网络模型相比，理论上更为完善。本文运用支持向量机建立时间序列预测模型，研究影响模型预测精度的相关参数，在分析参数对时间序列预测精度的影响基础上，提出用遗传算法建立支持向量机预测模型的参数自适应优化算法。最后，用算例表明了本文算法的正确性和有效性。

Optimizing of support vector machine time series forecasting model parameters based on genetic algorithms

Support Vector Machine(SVM) is based on Statistical Learning Theory(SLT) and Structural Risk Minimization Principle(SRM),and theoretically assures best model generalization.Therefore,it is more perfect in theory than Artificial Neural Network(ANN) that is based on Empirical Risk Minimization Principle(ERM).In this paper,SVM is used to establish time series forecasting model,study the parameters that influence forecasting accuracy.On the basis of analyzing model parameters' influence,a self-adaptive optimizing algorithm for establishing the model parameters based on genetic algorithm is put forward.In the end,examples showing the correctness and validity of the proposed algorithm are given.