Influence analysis and self-adaptive optimization of support vector machine time series forecasting model parameters

Support Vector Machine(SVM) is based on Statistical Learning Theory(SLT) and Structural Risk Minimization Principle(SRM),and theoretically assures best generalization,therefore,it is theoretically better than Artificial Neural Network(ANN) which is based on Empirical Risk Minimization Principle(ERM).In this paper,SVM was used to establish time series forecasting model,and on the basis of analyzing the influence of model parameters,a self-adaptive optimizing algorithm based on genetic algorithm was put forward.Finally,the sunspot data and the spectrometric oil data of some aero-engines were used for preliminary analysis,and the results show the correctness and validity of the new method.