Abstract
As single variable cannot provide comprehensive forecast information, Combining with the communication traffic data come from Xinjiang branch of china mobile communication corporation, the traffic and the four variables highly related with the traffic are done principal component analysis, the first principal component and the second principal component that they are obtained by principal components analysis (PCA) and dimension reduction are as input variables of ESNs. Therefore, the busy-time traffic forecasting model based ESNs (echo state networks) of multivariate principal component analysis was established. The simulation results show that the new prediction method has good generalization performance. Compared with single variable ESNs prediction model and LS-SVM forecasting model, the presented traffic forecasting method ensure the prediction efficiency while improving the prediction accuracy.
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