Time series prediction is one of the main research topics in time series analysis, which is of great importance both in theoretical and application aspects. To improve the performance of the wavelet-neural network model on complex time series, a novel multi-factor prediction model is proposed. According to the adaptability of different wavelet function to hydrological time series, a new criterion for the selection of different wavelet functions is also put forward, which is based on weighted correlation coefficients. Lastly, the newly proposed method has been tested on predicting the daily flow of WANGJIABA station, which is a very important observation site on HUAIHE river. It is found that the chosen Haar wavelet and B3 spline wavelet can produce higher prediction accuracy, which validates the effectiveness of the selecting principle of wavelet function. By comparing with traditional wavelet neural network for single time series, at least 10％ improvement has been observed for different predicting periods, and 15％ improvement in forecasting the high flow direction during the disastrous flood period. All the experimental results have shown that the proposed multi-factor prediction model is effective for complex hydrological time series prediction.