ETCN-NNC-LB: Ensemble TCNs With L-BFGS-B Optimized No Negative Constraint-Based Forecasting for Network Traffic

With the increasing demand for Internet access and the advent of technologies such as 6G and IoT, efficient and dynamic network resource management has become crucial. Accurate network traffic prediction plays a pivotal role in this context. However, existing prediction methods often struggle with challenges such as complexity-accuracy trade-offs, limited data availability, and diverse traffic patterns, especially in coarse-grained forecasting. To address these issues, this article proposes ETCN-NNC-LB, which is a novel ensemble learning method for network traffic forecasting. ETCN-NNC-LB combines Temporal Convolutional Networks (TCNs) with No Negative Constraint Theory (NNCT) weight integration in ensemble learning and is optimized using the Limited-memory Broyden-Fletcher–Goldfarb-Shanno with Box constraints (L-BFGS-B) algorithm. This method balances model complexity and accuracy, mitigates overfitting risks, and flexibly aggregates predictions. The ETCN-NNC-LB model also incorporates a pattern-handling method to forecast traffic behaviors robustly. Experiments on a real-world dataset demonstrate that ETCN-NNC-LB significantly outperforms state-of-the-art methods, achieving an approximately 22% reduction in the Root Mean Square Error (RMSE). The proposed method provides accurate and efficient network traffic prediction in dynamic, data-constrained environments.