Network Traffic Forecasting Using Machine Learning and Statistical Regression Methods Combined With Different Time Lags
In this study, different machine learning methods including Support Vector Machines (SVM), Radial Basis
Function (RBF) Neural Network, Multilayer Percpetron (MLP), M5P (a decision tree with linear regression functions at the
nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Pruning Error (REPTree), and a statistical regression
called Holt-Winters have been used to forecast the amount of network traffic in Transmission Control Protocol/Internet
Protocol (TCP/IP) -based networks. Two different Internet Service Providers' (ISPs) traffic data have been utilized to
develop traffic forecasting models. By using different time lags along with the aforementioned methods on the data sets,
several Internet traffic forecasting models have been built. The performance of the forecasting models for the data sets has
been assessed using Mean Absolute Percentage Error (MAPE). The results show that SVM and M5P based models usually
perform better than other models.
Keywords- Machine Learning, Time Series, Traffic Engineering, Time Lags.