Ten lectures on wavelets
Multiresolution learning paradigm and signal prediction
IEEE Transactions on Signal Processing
Real-time VBR video traffic prediction for dynamic bandwidth allocation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Network traffic prediction based on wavelet transform and season ARIMA model
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
A High-Grained Traffic Prediction for Microseconds Power Control in Energy-Aware Routers
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
A study on micro level traffic prediction for energy-aware routers
ACM SIGOPS Operating Systems Review
| Hi-index | 0.00 |
The presence of the complex scaling behavior in network traffic makes accurate forecasting of the traffic a challenging task. In this paper we propose a multiscale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple timescales using the à 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. This multiscale decomposition approach can better capture the correlation structure of 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 generally outperforms traffic prediction using neural network approach and gives more accurate result. The complexity of the prediction algorithm is also significantly lower than that using neural network.