Combining Least Squares Support Vector Machines and Wavelet Transform to Predict Gas Emission Amount

Author

Jia, Cunliang ; Wu, Haishan

Author_Institution

Coll. of Inf. & Electron. Eng., China Univ. of Min. & Technol., Xuzhou

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6097

Lastpage

6101

Abstract

To improve the prediction accuracy of gas emission amount, a novel model based on least squares support vector machines (LS-SVM) and wavelet transform (WT) is presented. First, the historical series is decomposed by wavelet, and thus the approximate part and several detail parts are obtained. Then each part is predicted by a separate LS-SVM predictor. The reconstruction of predicted series is used as the final prediction result. The selections of embedding dimension and decomposition level are discussed, respectively. The results show that this model has greater generality ability and higher accuracy

Keywords

chemical engineering computing; gases; least squares approximations; mining; support vector machines; time series; wavelet transforms; historical series decomposition; least squares support vector machines; predict gas emission amount prediction; predicted series reconstruction; wavelet transform; Accuracy; Educational institutions; Least squares approximation; Least squares methods; Neural networks; Predictive models; Risk management; Signal processing; Support vector machines; Wavelet transforms; Least squares support vector machines; gas emission prediction; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714252

Filename

1714252