The nonlinear-functional approximation ability of Radial Basis Function neural network leads us to forecast the time series of Shenzhen Energy A, and the one-step stock-price forecast is undertaken. The results by different methods are compared with respect to their performance on prediction. Results of prediction experiments with real data prove the efficiency of our prediction method based on Radial Basis Function neural network.