Abstract: Traffic measurement and modeling is essential to planning and management of existing IP networks，as well as to designing next generation networks.In this paper，a new P2P traffic prediction model based on wavelet transform and times series analysis is introduced.The original traffic metric series are decomposed and reconstructed into several branches by wavelet.These branches，stabilized by a statistic method，are predicted by ARIMA or ARMA models respectively according to their different features.The final predicted results can be obtained by reconstructing these predicted values of branches and an optimizing method with the use of dynamic exponential smoothing model.Experimental results show that forecasting trends are very similar with the actual flow curves，and the Mean Relative Error（MRE） is around 4.04%，more accurate than the traditional methods.

摘要： 网络流量测量与建模对网络管理有着重要的意义。为了合理规划P2P网络资源，提出了一种基于小波与时间序列分析的P2P流量预测模型。通过对原始序列的小波分解与单支重构，并使用了所提出的一种统计分析方法对流量进行平稳化处理，针对各分支特点分别采用ARMA和ARIMA模型进行预测，最后组合各分支的预测结果获得最终预测值，并对该预测值使用动态指数平滑模型进行优化。实验结果表明，对比已有的方法，这种方法具有更高的预测精度。