Prediction of web traffic based on wavelet and neural network

To improve the predication accuracy for web traffic, a predication method was proposed based on the integration of wavelet analysis and neural network. The web traffic time series, which is nonlinear and non-stationary, was decomposed and, then, reconstructed into several branches by the wavelet method. These branches were predicted by neural networks respectively and the final value was the combination of these predicted results. Theoretical analysis and experiment results show that wavelet analysis can decompose the original traffic series into several time serials that have simpler frequency components and are easier to be forecasted. So the method has higher predictive precision than traditional prediction approaches.