{"type":"ScholarlyArticle","title":"FreqLLM: Frequency-Aware Large Language Models for Time Series Forecasting","authors":[{"name":"Shunnan Wang","slug":"shunnan-wang","given":"Shunnan","family":"Wang","affiliation":""},{"name":"Min Gao","slug":"min-gao","given":"Min","family":"Gao","affiliation":""},{"name":"Zongwei Wang","slug":"zongwei-wang","given":"Zongwei","family":"Wang","affiliation":""},{"name":"Yibing Bai","slug":"yibing-bai","given":"Yibing","family":"Bai","affiliation":""},{"name":"Feng Jiang","slug":"feng-jiang","given":"Feng","family":"Jiang","affiliation":""},{"name":"Guansong Pang","slug":"guansong-pang","given":"Guansong","family":"Pang","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/377","url":"https://ijcai.org/proceedings/2025/377","pdf":"https://ijcai.org/proceedings/2025/0377.pdf","markdown":"https://ijcai.org/proceedings/2025/377.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/377","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":377,"pages":{"first":3389,"last":3397},"keywords":["Machine Learning: ML: Regression","Machine Learning: ML: Time series and data streams","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}