基于GA和LS-SVM的时间序列预测

Time series prediction based on GA and LS-SVM

支持向量机是在统计学习理论基础上提出的一种全新的机器学习方法。由于其出色的学习性能,该技术已经成为机器学习界的研究热点。研究了基于最小二乘支持向量机的建模方法,并用遗传算法自动获取最小二乘支持向量机的最优参数。在Matlab中ANFIS方法在输入维数大于5时就不予计算,而本建模方法则能够处理高维输入的非线性系统。并将其应用到十维Mackey-Glass混沌时间序列的预测中。结果表明,该方法具有自动获取最优参数、训练速度快、精度高、泛化能力强等优点。

Support Vector Machine （SVM） is a new machine learning method based on Statistical Learning Theory, which has become the hotspot of machine learning because of its excellent learning performance. In this paper, modeling method based on Least Square Support Vector Machine （ LS - SVM） is proposed, and Genetic Algorithm （GA） is used to optimize the parameters of LS- SVM model. The method can be applied to predict the ten - dimension Mackey- Glass Chaotic Time Series, which can not be computed by ANFIS, a mean only predict series with dimension less than five in Matlab; The result indicates that the method possesses the virtue of obtaining the optimal parameters automatically, high training speed, high precision, strong generalization ability etc.