关键词： 混沌时间序列, 相空间重构, 支持向量机, 均方误差

Abstract: Support vector machine is a learning technique based on the stuctural risk minimization principle,and it is a class of regression method with good generalization ability.Based on chaotic  time series characteristic,a prediction model of chaos time series is built by using the support vector machine.In this paper,the method,the characteristic,and the selecting of the key parameters are discussed about the model.A simulation example is taken to demonstrate correctness and effectiveness of the proposed method.The result shows that the model can better process a complex chaos time series data,and has better generalization and prediction accuracy.

Key words: chaotic time series, phase space reconstruction, support vector machine, mean square error