Adapting Large Language Models to Forecast in Frequency Domain

Abstract

Large language models (LLMs) have recently been applied to time series forecasting to leverage their reasoning and pattern recognition capabilities. Compared to task-specific forecasting models, LLMs exhibit generalizability and a broad understanding of cross-domain knowledge. However, current LLM-based forecasting methods overlook the importance of frequency properties in sequence data, which is a critical aspect in time series analysis. In this work, we propose an approach to incorporate frequency domain representation and operations into an LLM-based forecasting framework. We transform the label sequences into Fourier complex-valued representations and adapt LLMs to forecast in the frequency domain. To enhance frequency analysis and prediction, a Fourier neural network is introduced in the LLM-based forecasting. Extensive experiments verify that our approach compares favorably against the state-of-the-art methods in time series forecasting.