Traffic Decomposed Model on Wavelet in a Large-Scale Network

Traffic behavior in a large-scale network is very perplexing, so far the research on traffic behavior doesnt have a well-rounded method. By multi-resolution analysis, the complex traffic time series can be decomposed into many different frequent components. In the paper, based on the wavelet decomposed and recomposed theory, the backbone network traffic are decomposed wavelet coefficients and scale coefficients of different scales. Then the top layer low frequency and all layers high frequency time series are recomposed. And the trend term, period term and random term can be decomposed from these recomposed frequency series, so every sub-series can be analyzed and modeled separately. Finally, the traffic forecasting model can be built by recomposed the sub-series models. The model is proved through CERNET traffic and its precision is larger than ARIMA model.