Data traffic value forecasting using a time series model

This paper details the time series models for network traffic forecasting, in particular the Holt model and the use of a second-order polynomial model. This research is based on the understanding that accurate forecasts are important for effective network management, including aspects such as congestion control, resource allocation, and overall network performance optimization. Since the model selection process is based on traffic trends and the ability to predict them, it is important for preventing network congestion and optimizing resource allocation.By using the listed models in network data collection, it is possible to cover the evaluation of their predictive performance through an analytical process. In addition, this study contributes to a wider discussion of information on network management strategies, which emphasizes the importance of selecting appropriate prediction models based on specific network characteristics and traffic patterns. To meet the increasing demands of network traffic management, forecasting methodologies need to be continuously improved. Thus, this paper details time series modeling techniques for network traffic forecasting, highlighting the important role of accurate traffic forecasting in optimizing network resources and management techniques. The implications of this study can lay the foundation for future research and development in network traffic analysis and forecasting to address some pressing problems, offering valuable insights to network administrators, schedulers, network management and traffic forecasting researchers.