We propose a new technique of using the least squares support vector machines (LS-SVMs) for making one-step and multi-step prediction of chaotic time series. The LS-SVM achieves higher generalization performance than traditional neural networks and provides an accurate chaotic time series prediction. Unlike neural networks' training that requires nonlinear optimization with the danger of getting stuck into local minima, training LS-SVM is equivalent to solving a set of linear equations. Thus it has fast convergence. The simulation results show that LS-SVM has much better potential in the field of chaotic time series prediction.
展开▼
机译：Zhongnan University of Economics and Law Researcher Publishes New Study Findings on Support Vector Machines (Financial Time Series Model Based on Least Squares Support Vector Machine Predictive Control Algorithm in Financial Market)
机译：New Findings Reported from Zhejiang University Describe Advances in Support Vector Machines (Real Time Motion Compensation Technology Based On Least Square Support Vector Machine Prediction)
机译：Closure to "Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence"
