Abstract: The traditional model such as nonlinear model could not adapt to the model of network traffic．So only considering these characteristics can researchers model the network traffic accurately．By combining the analyses on self-similarity,length distribution and period of network traffic,making use of the wavelet transform and time series model to predict the traffic,and finally,comparing the length distribution and periodic,we can know whether the prediction result is reasonable．Firstly,the characteristics of network traffic such as self-similar and stationary were analyzed．Secondly,based on the result of the first step,the model was constructed and prediction results were obtained through selecting wavelet transform and time series．Finally,taking advantages of the length distribution and period,the model’s flexibility and accuracy were verfied．Through some experiments,it is proved that our model can reduce some computing compared with w-farima model and reflect the short-dependence and long-dependence of network traffic.

Key words: Traffic characteristics,Wavelet transform,Traffic prediction