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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 985930, 12 pages
http://dx.doi.org/10.1155/2012/985930
Research Article

Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

1School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2School of Software and Microelectronics, Northwestern Polytechnical University, Xi’an 710072, China
3Science and Technology Commission, Aviation Industry Corporation of China, Beijing 100068, China

Received 27 July 2012; Revised 19 November 2012; Accepted 29 November 2012

Academic Editor: Huaguang Zhang

Copyright © 2012 Guo Yangming et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.