AGO-Based Time Series Prediction Method Using LS-SVR

Abstract

Fault or health condition prediction of complex system equipments has attracted more and more attention in recent years. Complex system equipments often show complex dynamic behavior and uncertainty, it is difficult to establish precise physical model. Therefore, the time series of complex equipments are often used to implement the prediction in practice. In this paper, in order to improve the prediction accuracy, based on grey system theory, accumulated generating operation (AGO) with raw time series is made to improve the data quality and regularity, and then inverse accumulated generating operation (IAGO) is performed to get the prediction results with the sequence, which is computed by LS-SVR. The results indicate preliminarily that the proposed method is an effective prediction method for its good prediction precision.