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

LIN Shu-kuan; ZHI Li-jia; ZHANG Shao-min; QIAO Jian-zhong; WANG Guo-ren; YU Ge

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

更新时间：2025-07-16

- A Multi-SVR Based Fuzzy Modeling Method for Non-Stationary Time Series
- Acta Electronica Sinica Vol. 34, Issue 10, Pages: 1929-1932(2006)
- 作者机构：
  
  东北大学信息科学与工程学院,辽宁,沈阳,110004
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP181
- Published：2006
- 稿件说明：
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LIN Shu-kuan, ZHI Li-jia, ZHANG Shao-min, et al. A Multi-SVR Based Fuzzy Modeling Method for Non-Stationary Time Series[J]. Acta Electronica Sinica, 2006, 34(10): 1929-1932.
DOI：

LIN Shu-kuan, ZHI Li-jia, ZHANG Shao-min, et al. A Multi-SVR Based Fuzzy Modeling Method for Non-Stationary Time Series[J]. Acta Electronica Sinica, 2006, 34(10): 1929-1932. DOI：

摘要

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.

Abstract

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.

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