Network Traffic Prediction using a Hybrid Deep Learning Method with CEEMDAN and Attention Mechanism

Accurately predicting network traffic is critical for real-time anomaly detection, improved service quality, and effective resource management. This research delves into the most recent developments in CEEMDAN-integrated hybrid deep learning approaches for network traffic prediction. CEEMDAN breaks down data on unpredictable and non-linear network connections into IMFs, therefore isolating important data components. Deep learning architectures enhanced with attention mechanisms which give relevant features top priority for enhanced prediction accuracy are further investigated using these IMFs. By means of advanced data decomposition and feature selection, studies using real-world network traffic datasets show that these hybrid approaches exceed conventional approaches, so addressing constraints. In order to offer precise and context-aware traffic predictions, this study highlights the potential of CEEMDAN and attention driven models, leading to the development of network management systems that are stronger and more flexible.