FDALLM: Traffic Data Prediction with Functional Data Analysis and Large Language Models

Yujie Sun, Xuyu Wang, Guanqun Cao, Shiwen Mao

Abstract: In communication network management, mobile traffic prediction is vital for ensuring efficient system operation. Despite considerable progresses in applying neural networks for traffic prediction, traditional models often struggle to handle high-dimensional and time-dependent data. This paper addresses these challenges by proposing a novel framework that constructs prompts to enhance the predictive ability of large language models (LLMs) and their understanding of traffic data. Specifically, we leverage functional data analysis (FDA), a superior technique to traditional methods, to preprocess traffic data and extract features. Through extensive experiments on various LLMs with a real-world dataset, we validate the effectiveness and scalability of our proposed method, with performance improvements of up to 23.53 % and 21.34 % in mean squared error (MSE) and mean absolute error (MAE), respectively. Our results indicate a significant advance in predictive performance, providing a promising approach for future traffic data analysis.

External IDs:dblp:conf/icc/SunWCM25