Deep Learning Network-Temporal Models For Traffic Prediction
This addresses the need for improved traffic prediction in network intelligent control and management, representing an incremental advance over existing methods.
The paper tackled the problem of predicting multivariate time series in network traffic by developing two deep learning models that learn both temporal patterns and network topological correlations, with the LLM-based model showing superior overall prediction and generalization performance, while the GAT model reduced prediction variance across time series and horizons.
Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. The intricate topological interdependency and complex temporal patterns in network data demand new model approaches. In this paper, based on a systematic multivariate time series model study, we present two deep learning models aiming for learning both temporal patterns and network topological correlations at the same time: a customized network-temporal graph attention network (GAT) model and a fine-tuned multi-modal large language model (LLM) with a clustering overture. Both models are studied against an LSTM model that already outperforms the statistical methods. Through extensive training and performance studies on a real-world network dataset, the LLM-based model demonstrates superior overall prediction and generalization performance, while the GAT model shows its strength in reducing prediction variance across the time series and horizons. More detailed analysis also reveals important insights into correlation variability and prediction distribution discrepancies over time series and different prediction horizons.