中文摘要：

小波分析的多尺度时频分析功能,可以用于复杂时间序列的建模与预测,但当数据中含有异常值时,常规的时间序列模型难以有效地挖掘数据的规律。基于此,本文将小波分析和稳健估计引入到时间序列的建模与预测中,利用多尺度小波分析理论在处理非平稳信号上的优势和稳健回归估计能消除观测数据中异常值的影响的特点,建立了一种基于小波分析的稳健估计水文时间序列模型。并将所建模型用于月径流预报,通过与自回归滑动平均（ARMA）模型、反向传播（BP）神经网络模型的对比分析表明该模型在满足一定预报精度的同时,可以保证方法的可靠性与结果的稳定性,具有广阔的应用前景。

英文摘要：

The multiple-scale time-frequency analysis based on wavelet decomposition is widely applied to modeling and forecasting of complicated time series, but a traditional time series model cannot effectively describe the structure of a series containing outliers. To solve this problem, we adopted robust regression estimation to improve the traditional wavelet model and developed a wavelet analysis-robust estimation hydrological time series model that can effectively eliminate the impacts of outliers in observed data by using a multiple-scale wavelet analysis theory and its efficient processing of non-stationary signals. This model was verified against the monthly runoff forecasts of the Auto-Regressive and Moving Average （ARMA） model and Back Propagation （BP） neural network model through comparative analysis. It shows that the model is not only more accurate but also has better reliability and stability and a wider application potential in hydrological forecasting.