Spatio-Temporal Network Traffic Prediction Based on an Adaptive Topology Gating Mechanism
In the context of the internet of things (IoT), backbone network traffic prediction is essential for intelligent resource allocation, dynamic traffic engineering, and network management. However, network traffic often exhibits strong nonlinearity, complex spatio-temporal dependencies, and significant sensitivity to network topology. Existing methods still suffer from limitations in long-sequence modeling, implicit topology learning, and effective spatio-temporal feature fusion. To address these challenges, this paper proposes ATGFormer, a spatio-temporal network traffic prediction model based on an adaptive topology gating mechanism. Specifically, the proposed model employs multi-scale pyramid encoding, adaptive graph convolution, and a topology-gated unit to capture both short-term and long-term temporal patterns, learn implicit topological dependencies, and dynamically fuse spatial and temporal features. A hierarchical decoder is further designed to aggregate multi-scale predictions for improved forecasting performance. Experimental results on the real-world Abilene and GEANT dataset demonstrate that the proposed model outperforms mainstream baselines in terms of prediction accuracy, long-sequence stability, and robustness. These results indicate that ATGFormer can provide accurate and robust traffic prediction for intelligent network management in Smart IoT environments.