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

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.