Enhancing Traffic Load Forecasting in 5G Networks: A Statistical and Temporal Feature Engineering Approach

The rapid advancement of 5G technology has significantly increased energy consumption, underscoring the need for advanced energy management solutions. Proactive energy management, which relies on accurate predictions of network load to enable timely adaptive actions, emerges as a key strategy in addressing this challenge. In this study, we introduce a refined approach to forecasting traffic load in 5G networks, emphasizing the integration of statistical and temporal feature engineering. This methodology is aimed at capturing the intricate spatial and temporal patterns inherent in network data, thereby enhancing prediction accuracy. Leveraging an existing dataset comprising measurements from 1,000 base stations, we enriched this dataset with a set of derived features that reflect both temporal dynamics and load characteristics. Utilizing this enriched dataset, we trained and validated a suite of predictive models. Our findings reveal a notable improvement in the accuracy of traffic load predictions, outperforming standard baseline models. This underscores the effectiveness of our feature engineering approach in refining the predictive capabilities of models, paving the way for more efficient and proactive energy management in 5G networks.