基于组合SVR的非平稳时间序列的模糊建模方法

A Multi-SVR Based Fuzzy Modeling Method for Non-Stationary Time Series

本文介绍一种对非平稳时间序列建模的新方法.参考Janos Abonyi提出的应用于时间序列的模糊分块算法,将该算法与改进的支持向量回归模型结合起来.首先,提出一种改进的支持向量回归的表达形式;然后,通过启发式的加权方法将模糊分块的信息与SVR结合起来;最后,提出一种基于组合SVR的建模方法.实验结果表明,本文提出的方法对于非平稳时间序列的建模具有较高的实用价值.

A new approach for modeling non-stationary time series was introduced in this paper. Combine the fuzzy segmentation which was proposed by Janos Abonyi with Support Vector Machines（SVMs） .Firstly, a modified Support Vector Regression （SVR） was proposed; Secondly, fuzzy segment information was combined with SVR by heuristic weighting method; Thirdly, we discussed a model based on multi-SVR. Experimental results show that the method proposed in this paper has great practical values for non-stationary time series modeling.