The Research On Traffic Forecasting Model Based On SVR

The forecasting of mobile telephone traffic has always been the major mobile operators'focus, it is of directive meaning to master the system capacity and network expansion and so on. SA-SVR is utilized to forecast mobile traffic in this paper, what's more, the tariff levels are combined in the model which improved the prediction accuracy. The contents are included in the article as follows:1. The common algorithms of time series analysis and prediction are analyzed, and experiments prove the advantages of the model put forward in this paper;2. Based on SVR's solid theoretical support and a strong ability of nonlinear regression fitting, the SVR model is used to forecast mobile traffic time series;3. This paper analyzes and discusses the influence of the various nuclear parameters selection methods of SVR on prediction model as well as the importance of selecting the nuclear parameters. Then the common methods of nuclear parameters selection are compared in this paper. For the first time, the traffic time series are predicted by the SVR mode with simulated annealing selecting nuclear parameters, which makes a very good prediction results;4. The importance of embedded dimension in the process of time series analysis and forecasting is discussed and analyzed through experiments in this paper, then the conclusions on the sequence's embedding dimension are drawn based on the experimental results;5. The Tariff-SASVR model for traffic forecasting is proposed for the first time in this paper. It takes good advantages of the relationship between tariff level and traffic and then extracts the general trend of traffic. The residual sequence is predicted by SASVR. The results show that the model has a pretty good forecasting effect.