Research on Time Series Forecasting Model Based on Support Vector Machines

Support vector machines, which are based on statistical learning theory and structural risk minimization principle, in theory, ensure the maximum generalization ability of the model. So compared with the neural network model established on the Empirical Risk Minimization principle, they are more comprehensive in theory. In this paper, it applies the support vector machine into building the time series forecasting model, studies the relevant parameters which have impact on the models to predicting accuracy. It offers the parameter adaptive optimization algorithm which supports vector machine prediction model by building on genetic algorithm, which is based on the analysis of the influence of the parameters on the time series forecasting accuracy.