Proceedings

FreqLLM: Frequency-Aware Large Language Models for Time Series Forecasting

Shunnan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, Guansong Pang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 3389-3397.

https://doi.org/10.24963/ijcai.2025/377

Keywords

  • Machine Learning: ML: Regression
  • Machine Learning: ML: Time series and data streams
  • Data Mining: DM: Mining spatial and/or temporal data

Abstract

Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive performance. We found that embedding frequency-domain signals smooths weight distributions and enhances structured correlations by clearly separating global trends (low-frequency components) from local variations (high-frequency components). Building on these insights, we propose FreqLLM, a novel framework that integrates frequency-domain semantic alignment into LLMs to refine prompts for improved time series analysis. By bridging the gap between frequency signals and textual embeddings, FreqLLM effectively captures multi-scale temporal patterns and provides more robust forecasting results. Extensive experiments on benchmark datasets demonstrate that FreqLLM outperforms state-of-the-art TSF methods in both accuracy and generalization. The code is available at https://github.com/biya0105/FreqLLM.

Shunnan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, and Guansong Pang. 2025. FreqLLM: Frequency-Aware Large Language Models for Time Series Forecasting. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 3389–3397. https://doi.org/10.24963/ijcai.2025/377
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