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基于小波的Web流量组合预测方法研究
引用本文:姚淑萍,胡昌振,郑链.基于小波的Web流量组合预测方法研究[J].中国矿业大学学报,2006,35(4):540-544.
作者姓名:姚淑萍  胡昌振  郑链
作者单位:[1]北京理工大学计算机网络攻防对抗实验室,北京100081 [2]北京理工大学机电工程学院,北京100081
摘    要:为了提高Web流量的预测精度,提出一种基于小波、神经网络和自回归的组合预测方法.首先将Web流量构造为2个相关序列:历史序列和相似值序列;对具有平稳特征的相似值序列用AR模型进行预测;对体现了Web流量非线性、非平稳特性的历史序列则经过小波分解与单支重构后,针对各分支特点分别采用神经网络和自回归模型预测;最后组合2条序列的预测结果获得最终预测值.理论分析与实验表明:组合预测方法可以充分利用与流量相关的多种数据关系;小波分析可以将历史序列分解为多层频率成分更加单纯、更加易于预测的时间序列.因而所建方法比传统的预测方法具有更高的预测精度.

关 键 词:Web流量  小波分析  组合预测  流量预测
文章编号:1000-1964(2006)04-0540-05
收稿时间:05 25 2005 12:00AM
修稿时间:2005年5月25日

Research on Combination Prediction of Web Traffic Based on Wavelets
YAO Shu-ping , HU Chang-zhen , ZHENG Lian.Research on Combination Prediction of Web Traffic Based on Wavelets[J].Journal of China University of Mining & Technology,2006,35(4):540-544.
Authors:YAO Shu-ping  HU Chang-zhen  ZHENG Lian
Affiliation:1. Lab of Computer Network Defense Technology, Beijing Institute of Technology, Beijing 100081, China;2 School of Meehatronie Engineering, Beijing Institute of Technology, Beijing 100081, China
Abstract:For improving the prediction accuracy of Web traffic,a novel combination prediction method was proposed based on the integration of the wavelet,neural network and the auto-regression(AR).Two correlative traffic series,history series and similar values series,were distilled from the web traffic data.The stationary similar values series was predicted by AR model.The nonlinear and non-stationary history series were decomposed and then reconstructed into several branches by wavelet.These branches were predicted by neural networks or AR models respectively according to their different features.The predicted results of the two series were combined into the final predicted value.The results show that the combination prediction can take advantage of diverse correlative data relationships.The wavelet analysis can decompose history series into several time serials that have simpler frequency components and are easier to be forecasted.So the method has better predictive precision compared with traditional prediction approaches.
Keywords:web traffic  wavelet analysis  combination prediction  traffic prediction
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