%0 Journal Article
%A DAI Hui-jun
%A QU Hua
%A ZHAO Jian-long
%A ZHAO Ji-hong
%T A Comprehensive Forecasting Model for Network Traffic Based on Morlet-SVR and ARIMA
%D 2016
%R 10.13190/j.jbupt.2016.02.011
%J Journal of Beijing University of Posts and Telecommunications
%P 53-57
%V 39
%N 2
%X <p>According to the nonlinear and multi-dimensional dynamic characteristics of network traffic, combined with the ability of multi-scale wavelet analysis, a comprehensive forecasting model based on Morlet-support vector regression (Morlet-SVR) and auto regressive integrated moving average (ARIMA) was proposed, in which Morlet-SVR and ARIMA are employed to forecast the approximate signal and the multi-scale detail signals respectively by use of Mallet wavelet decomposition and single reconstruction. The final prediction result is obtained by linear superposition of the layers. Simulations give out comparisons with radial basis function-support vector regression and ARIMA model respectively, the proposed model shows higher prediction accuracy by comparison with three error evaluation measurements.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2016.02.011