Optimizing Network Traffic Prediction for Network Digital Twins: The Impact of Look-Back Period and Forecast Horizon

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the 'look-back period' and 'forecast horizon' significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network.