摘要： 分析网络流量的行为特性并建立模型进行预测，对于网络管理以及安全预警具有重要意义。基于此，针对网络异常处理滞后、网络服务质量差等问题，研究多种经典流量预测方法，从流量特性、建模复杂性、预测精度及应用场景等多角度进行分析比较。实验结果证明，预测模型与具体场景密切相关，实际操作时需根据流量特性及预测目标选择合适的模型。

关键词: 时间序列, 小波变换, 神经网络, 预测精度, 复杂度

Abstract: Modeling and predicting of network traffic are important for internet management and security prediction. Considering about draggle disposal and bad service quality, different kinds of classic traffic prediction methods are reviewed. Comparison about their suitable data, modeling complexity, prediction precision and suitable scene are presented. Experimental results prove that prediction model should correlate to scene tightly. It need to choose different models according to the traffic characters and prediction target in practices.

Key words: time series, wavelet transform, nerual network, prediction precision, complexity

中图分类号: 

• N945