对网络流量的精确预测，可以准确把握网络运行趋势，及时防范网络故障。针对长期网络流量预测准确度低，收敛速度慢的问题，提出一种小波系数感知的网络流量预测(WCNTP)机制。借助重标极差(R/S)序列分析法初步评估网络流量在大时间尺度上的统计特性；利用离散小波变换将非平稳的网络流量分解为多个相对平稳的流量序列；利用分数自回归求和滑动(FARIMA)模型对网络流量进行预测。结果表明，所提机制在长期网络流量预测过程中，具有较高的准确度且收敛速度快，能够精确评估网络性能，在保证网络平稳运行的同时，提高网络服务质量。

Precise prediction of network traffic makes great contributions to grasping the network running trends and avoiding network failure. Aiming at the problem of low accuracy and slow convergence in the long-term network traffic prediction, a Wavelet Coefficient-aware Network Traffic Prediction (WCNTP) mechanism is proposed. By using Rescaled Range(R/S) sequence analysis, the statistical characteristics of network traffic on the large time scale are evaluated. Then the non-stationary network traffic is decomposed into a number of relatively stable network traffic sequences by discrete wavelet transform. Finally, the network traffic is predicted by using the Fractional Auto-Regressive Integration Moving Average(FARIMA) model. Results show that, the proposed mechanism has high accuracy and fast convergence speed in the process of long-term network traffic prediction, by which the network performance can be evaluated accurately, thereby improving the network service quality and ensuring the smooth operation of the network.