Deep Learning Network-Temporal Models For Traffic Prediction
arXiv:2603.11475v1 Announce Type: new 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
arXiv:2603.11475v1 Announce Type: new 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.
Executive Summary
This article presents two novel deep learning models for traffic prediction, a customized network-temporal graph attention network (GAT) model and a fine-tuned multi-modal large language model (LLM) with a clustering overture. 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. The study reveals important insights into correlation variability and prediction distribution discrepancies over time series and different prediction horizons. The findings have significant implications for emerging network intelligent control and management functions, particularly in multivariate time series analysis.
Key Points
- ▸ The article introduces two novel deep learning models for traffic prediction.
- ▸ The LLM-based model outperforms the GAT model in overall prediction and generalization performance.
- ▸ The GAT model excels in reducing prediction variance across the time series and horizons.
Merits
Strengths of Deep Learning Approach
The deep learning models presented in this article are well-suited for complex multivariate time series analysis, offering superior prediction and generalization performance compared to existing statistical-based and shallow machine learning models.
Insights into Correlation Variability
The study reveals important insights into correlation variability and prediction distribution discrepancies over time series and different prediction horizons, providing valuable information for network intelligent control and management functions.
Demerits
Limited Generalizability
The models presented in this article may not be generalizable to other types of time series data, and further research is needed to validate their performance on diverse datasets.
Computational Complexity
The deep learning models presented in this article are computationally intensive, which may limit their practical implementation in real-world scenarios.
Expert Commentary
The article presents a comprehensive study on deep learning models for traffic prediction, demonstrating their potential in complex multivariate time series analysis. However, the limited generalizability of the models and their computational complexity are significant concerns that need to be addressed in future research. The study's findings have significant implications for emerging network intelligent control and management functions, particularly in optimizing traffic flow and reducing congestion. The insights into correlation variability and prediction distribution discrepancies may inform policy decisions related to urban planning and infrastructure development.
Recommendations
- ✓ Future research should focus on developing more generalizable and computationally efficient deep learning models for traffic prediction.
- ✓ The study's findings should be validated on diverse datasets to ensure their applicability in real-world scenarios.
