关键词: 趋势预测, 非线性、非平稳时间序列, 最小二乘支持向量机（LS-SVM）, 经验模式分解（EMD）

Abstract: The trend forecasting of vibration signals is an important content of condition monitoring and fault diagnosis.The old method of identification of machinery system is not practicable because the non-linear and non-stationary character is becoming more and more prominent.A prediction modelling method based on Empirical Mode Decomposition（EMD） and Least Square Support Vector Machine（LS-SVM） is proposed.Firstly，the trend time series is adaptively decomposed into a series of stationary Intrinsic Mode Functions（IMF） in different scale space using EMD.Then the right parameter and kernel functions are chosen to build different LS-SVM respectively to each and every IMF.Finally，these forecasting results of each IMF are combined to obtain final forecasting result.The simulation results show that the hybrid method has faster speed，higher precision and greater generalization ability than that of the single LS-SVM method.

Key words: trend prediction, non-linear and non-stationary time series, Least Squares Support Vector Machine（LS-SVM）, Empirical Mode Decomposition（EMD）