Linear correlation-based sparseness method for time series prediction with LS-SVR

Yangming Guo, Yafei Zheng, Xiangtao Wang, Guanghan Bai

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

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.

源语言英语
主期刊名QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
1716-1720
页数5
DOI
出版状态已出版 - 2013
活动2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2013 - Sichuan, 中国
期限: 15 7月 201318 7月 2013

出版系列

姓名QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

会议

会议2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2013
国家/地区中国
Sichuan
时期15/07/1318/07/13

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