Network traffic prediction based on wavelet transform and season ARIMA model

Authors:
Yongtao Wei;Jinkuan Wang;Cuirong Wang
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, China;School of Information Science and Engineering, Northeastern University, Shenyang, China;School of Information Science and Engineering, Northeastern University, Shenyang, China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

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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.