TY - GEN
T1 - Linear correlation-based sparseness method for time series prediction with LS-SVR
AU - Guo, Yangming
AU - Zheng, Yafei
AU - Wang, Xiangtao
AU - Bai, Guanghan
PY - 2013
Y1 - 2013
N2 - Fault or health trend prediction using time series is an effective way to protect the safe operation of highly reliable systems. Least squares support vector regression (LS-SVR) has been widely applied in time series prediction. However there is one of the main drawbacks of LS-SVR, which is lack of sparseness. This drawback impacts on its application if the number of training samples is large. So a new pruning method based on linear correlation is proposed, which reduces the number of support vectors by judging the linearly correlation among the sample data after they are mapped into high dimension feature space. This method can efficiently control the loss of useful information of sample data, improve the generalization capability of prediction model and reduce the prediction time simultaneously. And it also avoids the difficulty of reasonable selection of parameters. Simulation experiment results show that the computing time and prediction accuracy are both satisfied with the approach, which proves the efficiency of the proposed method.
AB - Fault or health trend prediction using time series is an effective way to protect the safe operation of highly reliable systems. Least squares support vector regression (LS-SVR) has been widely applied in time series prediction. However there is one of the main drawbacks of LS-SVR, which is lack of sparseness. This drawback impacts on its application if the number of training samples is large. So a new pruning method based on linear correlation is proposed, which reduces the number of support vectors by judging the linearly correlation among the sample data after they are mapped into high dimension feature space. This method can efficiently control the loss of useful information of sample data, improve the generalization capability of prediction model and reduce the prediction time simultaneously. And it also avoids the difficulty of reasonable selection of parameters. Simulation experiment results show that the computing time and prediction accuracy are both satisfied with the approach, which proves the efficiency of the proposed method.
KW - Least Squares Support Vector Regression (LS-SVR)
KW - linear correlation
KW - sparseness
KW - time series prediction
UR - http://www.scopus.com/inward/record.url?scp=84890019902&partnerID=8YFLogxK
U2 - 10.1109/QR2MSE.2013.6625907
DO - 10.1109/QR2MSE.2013.6625907
M3 - 会议稿件
AN - SCOPUS:84890019902
SN - 9781479910144
T3 - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
SP - 1716
EP - 1720
BT - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
T2 - 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2013
Y2 - 15 July 2013 through 18 July 2013
ER -