A timescale decomposition approach to network traffic prediction

The presence of the complex scaling behavior in network traffic makes accurate traffic prediction a challenging task. Some conventional prediction tools such as the recursive least square method are not appropriate for network traffic prediction. In this paper we propose a timescale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple timescales using the a trous Haar wavelet transform. The wavelet coefficients and the scaling coefficients at each scale are predicted independently using the ARIMA model. The predicted wavelet coefficients and scaling coefficient are then combined to give the predicted traffic value. This timescale decomposition approach can better capture the correlation structure of the traffic caused by different network mechanisms, which may not be obvious when examining the raw data directly. The proposed prediction algorithm is applied to real network traffic. It is shown that the proposed algorithm outperforms traffic prediction algorithms in the literature and gives more accurate results.