Support vector machine prediction model based on chaos theory

被引：0

作者：

Liangong S. ^{[1
]}

Huixin W. ^{[1
]}

Zezhong Z. ^{[2
]}

机构：

[1] College of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450011, Henan

[2] School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450011, Henan

来源：

| 1600年 / Science and Engineering Research Support Society卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Chaos theory; Online public opinion; Phase space reconstruction; Support vector regression;

D O I：

10.14257/ijmue.2016.11.2.18

中图分类号：

学科分类号：

摘要：

In order to enhance prediction precision of online public opinion, it put forward a kind of online public opinion prediction model (PSR-SVR) with the combination of chaos theory and support vector regression. First of all, the original data of online public opinion were obtained throughout topic segmentation, hotspot extraction, and data aggregate. Then, time sequence of online public opinion was reconstructed throughout phase-space reconstruction. Finally, the reconstructed time sequence of online public opinion was input support vector regression for modeling and prediction, and then it was compared with other online public opinion prediction model by experiment. The result shows that compared with the contrast model, PSR-SVR improves the prediction precision and reliability of online public opinion, and the prediction results have certain practical value. © 2016 SERSC.

引用

页码：173 / 184

页数：11

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