Network Traffic Prediction: Apply the Transformer to Time Series Forecasting

Abstract

Along with the development of technology and social progress, the Internet is increasingly widely used in life. Mobile communication, fiber optic broadband, and other essential Internet networks have gradually become indispensable in everyday life. The task of further improving and optimizing the quality of Internet network links and improving the efficiency of Internet networks has been on the agenda. This paper proposed a deep learning-based network traffic prediction model, which can capture the characteristics of network traffic information changes by inputting past network traffic data to achieve the effect of future network traffic prediction. The model structure is flexible and variable, which improves the problems of other methods that cannot capture long time series prediction features and cannot parallelize the output. It also has apparent advantages in time complexity and model convergence speed without the evident disadvantage of time lag. Based on this network traffic prediction model, it can help Internet service providers optimize network resource allocation, improve network performance, and allow Internet data centers to provide abnormal network warnings and improve user service level agreements.