中文摘要：

针对LS—SVM应用于大样本时问序列预测时存在的计算复杂度高和泛化能力降低的问题，提出一种采用局部模型的时间序列预测方法。该方法采用K—means算法对训练样本进行聚类，并以VRC原则确定最佳聚类数，然后利用LS—SVM对聚类后样本进行局部建模；同时，针对一般LS—SVM建模过程中共轭梯度方法计算效率低的问题，采用Cholseky分解方法以实现计算效率的提升。仿真实验和应用测试表明，该方法用于大规模数据分析时，可在保持预测精度的前提下，提高训练效率5～28倍，在降低计算复杂度的同时，有效地提高了模型的泛化能力。

英文摘要：

High computational complexity and low generalization ability seriously limit the forecasting application of LS-SVM in large scale time series. Aiming at this problem, a local modeling method called Clustering LS-SVM （CLS-SVM） is proposed. CLS-SVM uses the K-means algorithm to cluster time series dataset and adopts the vari- ance ratio criterion to find the optimal clustering number. Then in each cluster, local LS-SVM modeling is imple- mented using Cholseky decomposition method instead of Conjugate Gradient method to improve the efficiency in sol- ving the linear equation problem. Simulation experiment and real application test show that CLS-SVM can improve the modeling efficiency by 5 to 28 times without obvious precision dropping, and also effectively increase generaliza- tion ability and decrease computational complexity.