面向时序预测的支持向量回归参数选择方法

支持向量回归作为一种新的学习方法,在用于时间序列建模与预测时具有较好的泛化性能和预测能力.在支持向量回归建模的过程中,参数的选择对于模型的准确性至关重要.针对目前支持向量回归模型参数优化中存在的问题,提出一种面向时间序列预测的支持向量回归参数选择方法.根据时间序列及其预测的特点,对传统的交叉验证方法进行了改进,在保证时间序列预测方向性特征的基础上,充分挖掘有限样本所包含的信息,并将之与(-加权的支持向量回归相结合以选择好的模型参数.典型时间序列上的实验结果表明了所提出的支持向量回归参数选择方法的有效性,该方法在用于时间序列预测时取得了良好的效果.

Parameter Choosing Method of Support Vector Regression for Time Series Prediction

As a new learning method, Support Vector Regression (SVR) has good generalization and prediction performance for time series modeling and predicting. In the course of SVR modeling, parameter choosing is very important to the accuracy of models. Aimed at problems in parameter optimization of SVR models, the paper proposes an SVR parameter choosing method for time series prediction, which improves the traditional cross-validation according to the features of time series prediction and sufficiently mines information included in numbered samples on the basis of maintaining the direction characteristic of time series. Furthermore, it is combined with (-weighted SVR in order to get good model parameters. Experimental results over typical time series show the validity of the parameter choosing method of SVR. The method gets good effect applied to time series prediction.