用智能体迭代优化提示，让大模型直接预测时间序列。
FLAIRR-TS -- Forecasting LLM-Agents with Iterative Refinement and Retrieval for Time Series
- 构建双智能体系统：预测器生成初稿，提炼器结合历史输出与相似案例优化提示。
- 在多个基准数据集上优于静态提示和检索增强基线，接近专用提示性能。
- 无需代码生成或微调，适合快速部署到不同领域的时序预测任务。
利用大语言模型（LLMs）进行时间序列预测，需弥合数值模式与自然语言之间的鸿沟。传统方法依赖大量预处理与微调。近期研究表明，若提供精心设计的自然语言提示，冻结的LLM可媲美专用预测模型，但为每项任务手工设计提示成本高昂且缺乏通用性。本文提出FLAIRR-TS，一种测试时提示优化框架，采用智能体系统：预测器代理使用初始提示生成预测，随后由提炼器代理基于历史输出与检索到的相似案例对提示进行迭代优化。该自适应提示机制通过创造性提示模板实现跨领域泛化，无需中间代码生成，即可生成高质量预测。在多个基准数据集上的实验表明，其准确率超越静态提示与检索增强基线，逼近专用提示性能。FLAIRR-TS为模型调优提供了实用替代方案，通过智能体驱动的自适应提示优化与检索，实现强性能。
原文摘要 · Abstract (English)
Time series Forecasting with large languagemodels (LLMs) requires bridging numericalpatterns and natural language. Effective fore-casting on LLM often relies on extensive pre-processing and fine-tuning.Recent studiesshow that a frozen LLM can rival specializedforecasters when supplied with a carefully en-gineered natural-language prompt, but craft-ing such a prompt for each task is itself oner-ous and ad-hoc. We introduce FLAIRR-TS, atest-time prompt optimization framework thatutilizes an agentic system: a Forecaster-agentgenerates forecasts using an initial prompt,which is then refined by a refiner agent, in-formed by past outputs and retrieved analogs.This adaptive prompting generalizes across do-mains using creative prompt templates andgenerates high-quality forecasts without inter-mediate code generation.Experiments onbenchmark datasets show improved accuracyover static prompting and retrieval-augmentedbaselines, approaching the performance ofspecialized prompts.FLAIRR-TS providesa practical alternative to tuning, achievingstrong performance via its agentic approach toadaptive prompt refinement and retrieval.
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