FDALLM+: A Functional Data Analysis-Driven Large Language Model Framework for Network Traffic Prediction

Yujie Sun, Xuyu Wang, Guanqun Cao, Shiwen Mao

Abstract: In communication network management, prediction of mobile network traffic is essential to ensure efficient system operation. Although significant progress has been made in the application of neural networks to traffic prediction tasks, traditional models still face considerable challenges when handling high-dimensional and highly time-dependent data. To address these issues, this paper proposes a new prediction framework that leverages large language models (LLMs), by constructing efficient prompts to enhance the ability of large language models (LLMs) in traffic prediction and improve their understanding of complex traffic patterns. Specifically, we introduce functional data analysis (FDA), a technique that offers superior capabilities compared to traditional methods in processing continuous and high-dimensional data structures, to preprocess traffic data and extract key features. Extensive experiments conducted on multiple LLMs using a real-world dataset validate the effectiveness and scalability of the proposed method. The experimental results demonstrate that the framework achieves significant improvements in predictive performance, providing a promising and efficient solution for traffic data analysis in future communication networks.

External IDs:doi:10.1109/jiot.2025.3604332