From Statistics to Deep Learning: Forecasting Mobile Throughput - Santos, Estêvão J.; Almeida, Tiago A. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2025)
Accurate download throughput prediction is critical for adaptive resource management and QoS in 5G networks, particularly under high user mobility. This work systematically investigates two key design choices in time series forecasting: (i) local versus global models and (ii) the inclusion or exclusion of external covariates. We evaluate statistical, machine learning, and deep learning methods on real-world 5G data, where throughput is predicted using channel quality metrics and user speed as potential covariates. Experimental results show that global, tree-based ensembles like LightGBM achieve the best trade-off between accuracy, robustness, and efficiency. Furthermore, we found that the explored network quality covariates were insufficient to consistently improve performance for this complex task. All source code is available at: https://github.com/ejs94/5g-forecasting .