A spatio-temporal graph neural network-based method for optical network traffic forecasting and anomaly detection

Abstract

A Spatio -Temporal Graph Neural Network (STGNN) framework is presented for precise optical network traffic forecasting and anomaly detection. The model encodes network topology as a directed weighted graph, extracting node features that integrate static attributes and dynamic traffic states. The graph convolutional layer records the spatial relationship, while the temporal attention mechanism imitates the time-related changes according to the changes of traffic flow. Federated learning introduces collaborative model training without raw data exchange to address the scalability and privacy issues in multi-domain environments. Experimental evaluation on public and proprietary datasets shows that the proposed STGNN consistently outperforms statistical and deep learning baselines in terms of prediction accuracy and anomaly detection sensitivity. According to the component ablation study, each module must ensure stable performance. The integrated real-time monitoring and adaptive alarm architecture helps realize elastic and intelligent optical network management and helps quickly identify and mitigate anomalies. The complex traffic analysis and O&M assurance of the next-generation optical network are provided by this technical solution, which is scalable.