基于大模型文本推理的时间序列预测
Time Series Forecasting via Large Language Model-Based Textual Reasoning
刘霖 1廖建新1
作者信息
- 1. 北京邮电学校计算机学院，北京，100876
- 折叠
摘要
时间序列预测在各领域应用广泛。近年来，随着多模态技术的发展，通过大语言模型将文本信息融入时间序列预测任务的相关研究发展迅速。但利用外部文本文本辅助预测仍面临诸多挑战，文本与时间序列之间往往缺乏直接关联，且模态融合阶段难以有效利用时间序列的周期性及其他固有特征。本文为解决上述问题，研究一种能够更加有效利用外部文本辅助时间序列预测的模型。与将大语言模型作为时间序列预测模型骨干或仅用其对文本进行总结的现有方法不同，该模型借助大语言模型的知识与推理能力，生成与时间序列高度相关的预测文本，为时间序列趋势变化预测提供直接指导；同时通过低复杂度的多尺度模态融合方法，在不同频域层级捕捉时间序列与文本的关联，在保证计算效率的同时提升预测精度。在真实数据集上开展的大量实验验证了该模型能有效提升时间序列预测的效果。
Abstract
Time series forecasting has been widely applied across various domains. In recent years, with the advancement of multimodal technologies, research on integrating textual information into time series forecasting tasks through large language models (LLMs) has progressed rapidly. However, leveraging external textual data to assist forecasting still faces significant challenges: textual data and time series often lack direct correlation, and existing modality fusion approaches struggle to effectively exploit intrinsic characteristics of time series, such as periodicity. To address these issues, this study proposes a novel model that more effectively utilizes external text to enhance time series forecasting. Unlike existing methods that either treat LLMs as the backbone of the forecasting model or merely use them to summarize textual inputs, our approach harnesses the knowledge and reasoning capabilities of LLMs to generate prediction-oriented textual descriptions highly relevant to the target time series, thereby providing direct guidance for forecasting trend changes. Additionally, we introduce a low-complexity, multi-scale modality fusion mechanism that captures correlations between time series and text across different frequency-domain levels, improving forecasting accuracy while maintaining computational efficiency. Extensive experiments on real-world datasets demonstrate that the proposed model significantly enhances time series forecasting performance.关键词
计算机应用技术，时间序列预测，多模态融合。Key words
Computer Application Technology/Time Series Forecasting/Multimodal Fusion引用本文复制引用
刘霖,廖建新.基于大模型文本推理的时间序列预测[EB/OL].(2026-03-17)[2026-03-20].http://www.paper.edu.cn/releasepaper/content/202603-149.学科分类
计算技术、计算机技术
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