LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908761110609920 |
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| author | You, Jiacheng Yang, Jingcheng Xie, Yuhang Wu, Zhongxuan Li, Xiucheng Li, Feng Wang, Pengjie Xu, Jian Zheng, Bo Chen, Xinyang |
| author_facet | You, Jiacheng Yang, Jingcheng Xie, Yuhang Wu, Zhongxuan Li, Xiucheng Li, Feng Wang, Pengjie Xu, Jian Zheng, Bo Chen, Xinyang |
| contents | Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing deep forecasting models typically supervise predictions using full-length temporal windows, which include substantial high-frequency noise and obscure long-term trends. Moreover, auxiliary variables containing rich domain-specific information are often underutilized, especially in few-shot settings. To address these challenges, we propose LoFT-LLM, a frequency-aware forecasting pipeline that integrates low-frequency learning with semantic calibration via a large language model (LLM). Firstly, a Patch Low-Frequency forecasting Module (PLFM) extracts stable low-frequency trends from localized spectral patches. Secondly, a residual learner then models high-frequency variations. Finally, a fine-tuned LLM refines the predictions by incorporating auxiliary context and domain knowledge through structured natural language prompts. Extensive experiments on financial and energy datasets demonstrate that LoFT-LLM significantly outperforms strong baselines under both full-data and few-shot regimes, delivering superior accuracy, robustness, and interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models You, Jiacheng Yang, Jingcheng Xie, Yuhang Wu, Zhongxuan Li, Xiucheng Li, Feng Wang, Pengjie Xu, Jian Zheng, Bo Chen, Xinyang Machine Learning Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing deep forecasting models typically supervise predictions using full-length temporal windows, which include substantial high-frequency noise and obscure long-term trends. Moreover, auxiliary variables containing rich domain-specific information are often underutilized, especially in few-shot settings. To address these challenges, we propose LoFT-LLM, a frequency-aware forecasting pipeline that integrates low-frequency learning with semantic calibration via a large language model (LLM). Firstly, a Patch Low-Frequency forecasting Module (PLFM) extracts stable low-frequency trends from localized spectral patches. Secondly, a residual learner then models high-frequency variations. Finally, a fine-tuned LLM refines the predictions by incorporating auxiliary context and domain knowledge through structured natural language prompts. Extensive experiments on financial and energy datasets demonstrate that LoFT-LLM significantly outperforms strong baselines under both full-data and few-shot regimes, delivering superior accuracy, robustness, and interpretability. |
| title | LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.20002 |