With the increasing number of service types of wireless network and the increasingly obvious differentiation of quality of service (QoS) requirements, the traffic flow classification and traffic prediction technology are of great significance for wireless network to provide differentiated QoS guarantee. At present, the machine learning methods attract widespread attentions in the tuples based traffic flow classification as well as the time series based traffic flow prediction. However, most of the existing studies divide the traffic flow classification and traffic prediction into two independent processes, which leads to inaccurate classification and prediction results. Therefore, this paper proposes a method of joint wireless network traffic classification and traffic prediction based on machine learning. First, building different predictors based on traffic categories, so that different types of classified traffic can use more appropriate predictors for traffic prediction according to their categories. Secondly, the prediction results of different types of predictors, as a posteriori feature, are fed back to the classifiers as input features to improve the accuracy of the classifiers. The experimental results show that the proposed method has improves both the accuracy of traffic classification and traffic prediction in wireless networks.

翻译：随着无线网络业务类型日益增多，服务质量需求差异化愈发明显，流量分类与流量预测技术对于无线网络提供差异化服务质量保障具有重要意义。目前，基于元组的流量分类及基于时间序列的流量预测中，机器学习方法受到广泛关注。然而现有研究大多将流量分类与流量预测划分为两个独立过程，导致分类与预测结果不准确。为此，本文提出一种基于机器学习的无线网络流量分类与预测联合方法。首先，根据流量类别构建不同预测器，使不同类型的分类流量能够依据其类别采用更合适的预测器进行流量预测；其次，将不同预测器的预测结果作为后验特征反馈至分类器作为输入特征，以提高分类器精度。实验结果表明，所提方法在无线网络流量分类与流量预测的准确性上均取得提升。