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 squaresversion 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 aspolynomial, RBF's and MLP's can be used. Here we use LS-SVMs to time series prediction compared to radial basisfunction neural networks. We consider a noisy (Gaussian and uniform noise)Mackey-Glass time series. The resultsshow that least squares support vector machines is excellent for time series prediction even with high noise.

Prediction of Time Series Based on Least Squares Support Vector Machines

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.