数值实验表明，SVR方法对非平稳的金融时间序列具有良好的建模和泛化能力。
Numerical test results show that SVR has good ability of modeling nonstationary financial time series and good generalization under small data set available.
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。
Empirical mode decomposition is used for pre-processing. Decompose time series, then make models separately and combine all the values.
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。
Empirical mode decomposition is used for pre-processing. Decompose time series, then make models separately and combine all the values.
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