Abstract
This article has considered a variety of uncomplicated time-series models for the purpose of making predictions. Forecasting network traffic has been an area of significant interest, particularly for network congestion monitoring, data flow control, and network management. By carefully selecting a traffic model, the most crucial features of network traffic, including short--term and long-term dependent processes and self-similarity over extended time periods, have been able to be identified and predicted. The article has explored forecasting methods such as Holt's model and the second-order polynomial model.