%0 Journal Article
%T Prediction of Time Series Based on Least Squares Support Vector Machines<br>新型SVM对时间序列预测研究
%A ZHU Jia-Yuan DUAN Bao-Jun ZHANG Heng-Xi
%A <br>朱家元
%A 段宝君
%A 张恒喜
%J 计算机科学
%D 2003
%I 
%X 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.
%K Machine learning
%K Support vector machines
%K Statistical learning theory
%K Neural net works
%K Time series prediction<br>机器学习
%K 支持向量机
%K SVM
%K 时间序列预测
%K 模糊神经网络
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=3ADC3E87C71CE40F&yid=D43C4A19B2EE3C0A&vid=340AC2BF8E7AB4FD&iid=5D311CA918CA9A03&sid=2F56B21F91C9B05B&eid=F122871CC7EC92DC&journal_id=1002-137X&journal_name=计算机科学&referenced_num=3&reference_num=8