Accurate streamflow estimation remains a great challenge although diverse modeling techniques have been developed during recent decades. In contrast to the process-based models, the empirical data-driven methods are easy to operate, require low computing capacity and yield fairly accurate outcomes, among which the state-space (STATE) approach takes use of the temporal structures inherent in streamflow series and serves as a feasible solution for streamflow estimation. Yet this method has rarely been applied, neither its comparison with other methods. The objective was to compare the performance of an autoregressive STATE approach to the traditional multiple linear regression and artificial neural network in simulating annual streamflow series of 15 catchments located in the Loess Plateau of China. Annual data of streamflow (Q), precipitation (P) and potential evapotranspiration (PET) during 1961 ~ 2013 were collected. The results show that STATE was generally the most accurate method for Q estimation, explaining almost 90% of the total variance averaged over all the 15 catchments. The estimation of streamflow relied on its own of the previous year for most catchments. Besides, the impacts of P and PET on the temporal distribution of streamflow were almost equal. Missing data were estimated using the STATE method, which allowed inter-annual trend analysis of the streamflow. Significant downward trends were manifested at all the 15 catchments during the study period and the corresponding slopes ranged from -0.24 to -1.71 mm y -1 . These findings hold important implications for hydrological modelling and management in China's Loess Plateau and other arid and semi-arid regions.

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Time series prediction is widely used in industry engineering, finance, economy, traffic and many other fields. For power system, prediction is often concerned, and online prediction has significance to the system operation safely and steadily. An efficient method for online prediction of time series using wavelet decompositions and support vector machine is presented, which can improve the prediction accuracy. For online application, sliding window model and incremental algorithms for wavelet decompositions are used. This method has low cost in memory and run time, it can predict time series in high accuracy and less time. Simulation experiment using gas furnace time series dataset show the effectiveness of proposed method.