🤖 AI Summary
Existing statistical and shallow machine learning approaches struggle to effectively model the complex spatiotemporal dependencies inherent in multivariate network traffic. To address this limitation, this work proposes two deep learning methodologies: first, a tailored graph attention network (GAT) that jointly captures topological structure and temporal dynamics; and second, a novel framework that integrates clustering-based preprocessing with fine-tuned multimodal large language models (LLMs) for traffic forecasting—an approach not previously explored in this domain. Experimental results on real-world datasets demonstrate that the LLM-based method achieves superior overall accuracy and generalization performance, while the GAT significantly reduces prediction variance across diverse sequences and time horizons, thereby overcoming key constraints of conventional time-series models.
📝 Abstract
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.