Explaining Aggregated Network Traffic Predictors

Paper in proceeding, 2025

Network traffic prediction is essential for the intelligent management of modern backbone networks. In application-aware settings, it becomes crucial to generate detailed forecasts for each traffic class to ensure they are handled with appropriate care. To address scalability and survivability challenges, models built using data aggregation techniques offer an effective solution. In this paper, we examine how such models operate to make successful forecasts for diverse traffic classes in real and semi-synthetic data, incorporating Explainable Artificial Intelligence (XAI) tools. The analysis reveals interesting trends in how various regressors capture cross-class intricacies and correlations, highlighting the potential of aggregated models.