Because stock forecasting is a uncertain, nonlinear and nonstationary time series problem, it is difficult to achieve a satisfying prediction effect by traditional methods.
由于股票预测是不确定、非线性、非平稳的时间序列问题，传统的方法往往难以取得满意的预测效果。
2
A state space approach for the modeling of nonstationary time series is presented.
非平稳时间序列的状态空间建模技术被用于陀螺漂移分析。
3
Numerical test results show that SVR has good ability of modeling nonstationary financial time series and good generalization under small data set available.