The space of prediction and application of non-stationary time series were expanded through the combined model of wavelet analysis, gray and time series prediction methods.
将小波分析理论、灰色预测理论和时间序列预测法组合进行需水量的预测，为原始非平稳时间序列的预测应用拓展了空间。
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。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
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