支持向量机理论是20世纪90年代由Vapnik提出的一种基于统计学习理论的新的机器学习方法,其具有全局最优解和较好的泛化能力,可将其用于求解时间序列预测间题.但是对于非平稳时间序列的顶测,利用支持向量机算法单独建立一个模型的预测结果不如平稳时间序列那样明显,可以采用经验模式分解法作为时序预测的预处理工具.先将非平稳时间序列进行经验模式分解,再对各个分量分别建模,最后将各分量预测结果进行组合.同时通过仿真实验验证了该方法是有效的.%Support vector machines, which was proposed by Vatnik in 1990s, is a new machine learning method based on statistical learning theory(SLT). It has global optimum and preferable generalization, can be used on time series prediction. When predicting non -stationary time series, the results show that the result from single model is not as distinct as the stationary time series. Empirical mode decomposition is used for pre-processing. Decompose time series, then make models separately and combine all the values. Simulation results demonstrate that the proposed method is valuable.
展开▼
