Investigation of Different Wavelet Bases for LRD Traffic Prediction

K. Mizanian and M. Vasef (Iran)


Long Range Dependence, Self Similarity, Traffic Prediction, RLS, Wavelet


Leland showed in his breakthrough paper that the traffic data exhibits a high degree of Long Range Dependence (LRD) in addition to Short Range Dependence (SRD). The prediction of network traffic with complex correlation structure that is characterized by LRD as well as SRD is one of today's high speed networks main problems. Choosing the "suitable" wavelet bases that do this reduction quickly, can lead to more accurate predictors. Previous work uses only Daubechies 40 wavelet basis. In his paper we choose the "suitable" wavelet bases experimentally. The mean absolute error is measured as a performance parameter. According to simulation results, two different wavelet bases, Coiflet 5 and Symlet 20 with adopted Recursive Least Squares (RLS) are more accurate predictors than Daubechies 40 for prediction of LRD network traffic.

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