基于支持向量机的混沌时间序列预测

支持向量机（SVM）是一种基于结构风险最小化原理的学习技术，是一种具有很好泛化性能的回归方法.针对混沌时间序列特点，提出混沌时间序列预测的支持向量机建模的思路、特点及关键参数的选取.对模型进行了实例研究，结果表明该模型能较好地处理混沌时间序列，具有较高的泛化能力和很好的预测精度.

Support Vector Machines for the Chaotic Time Series Prediction

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.