Time Series Weighted Prediction Method Using Multikernel LS-SVR
Fault or health condition prediction of complex systems has attracted more and more attention in recent years.Due to complex dynamic behavior and uncertainty in running,complex systems are always difficult to establish precise physical model.Therefore,the time series of complex equipments are often used to perform the prediction in practice.In order to satisfy the requirements of application,which are good prediction accuracy and less calculation time,we utilize multiple relevant time series and propose a new prediction method based on multikernel LS-SVR.In this method,we proposed a simple computational method to obtain combining weights of multikernel,and the new prediction model considers the different effect of historical data into the prediction process.Prediction experiment is made by two relevant time series of one complex avionics equipment.The results indicate preliminarily that the proposed method is a practical and effective prediction method for its good prediction precision within less time cost.
Time series Weighted prediction Least squares support vector regression (LS-SVR) Multiple kernel learning (MKL)
Guo-Chang Zhou Yang-Ming Guo Jie-Zhong Ma
Academy of Space Technology, Xi’an, China School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an, China
国际会议
西安
英文
251-260
2013-11-25（万方平台首次上网日期，不代表论文的发表时间）