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With the increasing number of service types of wireless network and theincreasingly obvious differentiation of quality of service (QoS) requirements,the traffic flow classification and traffic prediction technology are of greatsignificance for wireless network to provide differentiated QoS guarantee. Atpresent, the machine learning methods attract widespread attentions in thetuples based traffic flow classification as well as the time series basedtraffic flow prediction. However, most of the existing studies divide thetraffic flow classification and traffic prediction into two independentprocesses, which leads to inaccurate classification and prediction results.Therefore, this paper proposes a method of joint wireless network trafficclassification and traffic prediction based on machine learning. First,building different predictors based on traffic categories, so that differenttypes of classified traffic can use more appropriate predictors for trafficprediction according to their categories. Secondly, the prediction results ofdifferent types of predictors, as a posteriori feature, are fed back to theclassifiers as input features to improve the accuracy of the classifiers. Theexperimental results show that the proposed method has improves both theaccuracy of traffic classification and traffic prediction in wireless networks.

A method of combining traffic classification and traffic prediction based on machine learning in wireless networks