计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 289-293.doi: 10.11896/jsjkx.200400056
向昌盛1, 陈志刚2
XIANG Chang-sheng1, CHEN Zhi-gang2
摘要： 针对网络流量的混沌特性以及海量特性,为弥补网络流量预测模型存在的不足,以获得更优的网络流量预测结果,提出了面向海量数据的网络流量混沌预测模型。该模型首先采用小波分析对原始网络流量时间序列进行多尺度处理,得到不同特征的网络流量分量,然后对网络流量分量的混沌特性进行分析,分别进行重构,并采用机器学习算法中的极限学习机进行建模与预测,最后采用小波分析对网络流量分量的预测结果进行叠加,得到原始网络流量数据的预测值,并进行网络流量预测的仿真实验。实验结果表明,所提模型的网络流量预测精度超过90%,不仅预测精度结果远远超过其他网络流量预测模型的结果,而且其网络流量预测的结果更加稳定,因此是一种有效的网络流量建模与预测工具。
中图分类号:
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