An Adaptive Framework for Generalizing Network Traffic Prediction Towards Uncertain Environments

We developed a new framework using time-series analysis for dynamically assigning mobile network traffic pre-diction models in previously unseen wireless environments. Our framework selectively employs learned behaviors, outperforming any single model with over a 50% improvement relative to current studies. More importantly, it surpasses traditional approaches without needing prior knowledge of a cell, all while remaining model agnostic. The framework begins with unsupervised clustering of time-series data to identify unique trends. Subsequently, we apply supervised learning for traffic volume prediction within each cluster. This specialization towards specific traffic behaviors occurs without penalties from spatial and temporal variations. Finally, the framework adaptively assigns trained models to new, previously unseen cells. By analyzing real-time measurements of a cell, our framework intelligently selects the most suitable cluster for that cell at any given time, with cluster assignment dynamically adjusting to spatio-temporal fluctuations.