Title :
An EMD-SVR method for non-stationary time series prediction
Author :
Jun Fan ; Yanzhen Tang
Author_Institution :
Coll. of Inf. Syst. & Manage., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
In the area of prognostics and health management, data-driven methods increasingly show the superiority against model-based method due to the complex relationships and learn trends available in the data captured without the need for specific failure models. This paper uses Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) to build a model for non-stationary time series prediction. And it proves that the EMD-SVR method can solve the problem of few training samples in modeling the path of performance degradation. Then when the threshold is given, we can forecast the lifetime of engineering systems based on the performance degradation data.
Keywords :
failure analysis; prediction theory; support vector machines; time series; EMD-SVR method; SVM; data-driven methods; empirical mode decomposition; engineering systems; failure models; health management; model-based method; nonstationary time series prediction; performance degradation data; prognostics; support vector machine; Data models; Market research; Predictive models; Reliability; Support vector machines; Time series analysis; Wheels; empirical mode decomposition (EMD); long-term prediction; non-stationary time series; performance degradation; support vector machine (SVM);
Conference_Titel :
Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-1014-4
DOI :
10.1109/QR2MSE.2013.6625918