  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.
