Multi-scale Internet Traffic Prediction Using Wavelet Neural Network Combined Model
Di Chen, Feng Hai-liang, Qing-jia Lin, Chen Chun-xiao · 2006
Internet traffic belongs to non-stationary time series, wavelet transform can decompose non-stationary time series into several stationary components. In this paper, we decompose the Internet traffic with wavelet first, and then apply two different artificial neural network (ANN) architectures, linear neural network (LNN) and Elman neural network (ENN), to predict the components. The LNN predicts the linear data, whereas the ENN predicts the nonlinear data. To enhance the prediction accuracy and merge the traffic characteristics captured by individual models, the outputs of the individual ANN predictors are combined using three networks respectively. They are back propagation neural network (BPNN), LNN and ENN. The problem of one-step-ahead traffic prediction at different timescales is considered. The results indicate that the proposed combined model outperforms the individual models and the wavelet transform improves the performance further. The results also show that the prediction performance depends on the traffic nature and the considered timescale