Research on network traffic prediction based on multi-feature fusion clustering and XGBoost model

Abstract

With the development of mobile network technology, network traffic has not only experienced exponential explosive growth, but also its application scenarios have become more and more extensive. It is a challenging proposition to find an efficient and accurate matching prediction model based on massive idiosyncratic data. This scheme proposes to introduce EMD modal decomposition to decompose the local feature components of data based on the time-scale features of the data itself, to do cluster analysis on the components by K-mean clustering algorithm, and then to model and predict the clustered local feature components by XGBoost model, so as to reduce the data dimensionality and prediction complexity. The results show that the modeling and prediction of the clustered local feature components using XGBoost model effectively improves the model prediction accuracy.