Nonlinear time series fault prediction based on clustering and support vector machines

Based on clustering and support vector machines, a new method is proposed to solve the nonlinear time series fault prediction. The normal time series is clustered using K-means clustering algorithm to get the normal prototype. Meanwhile, the predicting series is obtained by time series predicting algorithm based on support vector regression. Fault prediction can also be implemented by calculating the similarity between the normal prototype and the predicting series. Finally, the simulation results indicate that the proposed method can predict the fault more quickly and more accurately.