Prediction of Time Series Based on Least Squares Support Vector Machines
新型SVM对时间序列预测研究

In this paper, we present a new support vector machines - least squares support vector machines (LS.-SVMs). While standard SVMs solutions involve solving quadratic or linear programming problems, the least squares version of SVMs corresponds to solving a set of linear equations, due to equality instead of inequality constraints in the problem formulation. In LS-SVMs, Mercer condition is still applicable. Hence several type of kernels such as polynomial, RBF's and MLP's can be used. Here we use LS-SVMs to time series prediction compared to radial basis function neural networks. We consider a noisy (Gaussian and uniform noise)Mackey - Glass time series. The results show that least squares support vector machines is excellent for time series prediction even with high noise.