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

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.