Network traffic prediction based on wavelet transform and season ARIMA model

Abstract

To deal with the characteristic of network traffic, a prediction algorithm based on wavelet transform and Season ARIMA model is introduced in this paper. The complex correlation structure of the network history traffic is exploited with wavelet method .For the traffic series under different time scale, self-similarity is analyzed and different prediction model is selected for predicting. The result series is reconstructed with wavelet method. Simulation results show that the proposed method can achieve higher prediction accuracy rather than single prediction model. © 2011 Springer-Verlag.