A hybrid support vector regression for time series forecasting

被引：1

作者：

Xiang, Ling ^{[1
]}

Zhu, Yongli ^{[1
]}

Tang, Gui-ji ^{[1
]}

机构：

[1] N China Elect Power Univ, Dept Mech Engn, Minist Educ, Key Lab Condit Monitoring & Control Power Plant E, Baoding 071003, Hebei Province, Peoples R China

来源：

2009 WRI WORLD CONGRESS ON SOFTWARE ENGINEERING, VOL 4, PROCEEDINGS | 2009年

关键词：

Hybrid model; Support vector regression (SVR); Neural networks; Time series prediction;

D O I：

10.1109/WCSE.2009.130

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The study applies a novel neural network technique, hybrid support vector regression (SVR), to forecast values of the turbo-generator vibration in time series. The simulation experiment results showed that the hybrid model is superior to the individual models for the test. Most of the individual models evaluated showed poor ability to detect directional change. This problem can be overcome with the use of the hybrid model. Besides superior turning point delectability, the hybrid model could achieve superior predictive performance and showed promising results. Therefore, the results suggested that the proposed hybrid model is typically a reliable forecasting tool for application within the forecasting fields of time series.

引用

页码：161 / 165

页数：5

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