---
id: 20260730-T0-09
title: "LLM作为时序预测规划器：无需训练即可利用文本条件"
title_en: "LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models"
url: https://ai.daily.yangsir.net/daily/20260730-T0-09
issue_date: 2026-07-30
publish_date: 2026-07-29T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2607.24892
---

# LLM作为时序预测规划器：无需训练即可利用文本条件

arXiv 论文提出一种方法，使LLM能够在无需微调的情况下，作为时序预测模型的规划器。它利用LLM的文字理解能力，将事件描述、政策变动等文本信息编码为条件输入，使时序基础模型能基于文本上下文进行预测。例如，模型可以根据“央行加息公告”文本调整利率预测，且无需额外训练。

## English Version

**LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models**

An arXiv paper proposes a method that uses LLMs as a training-free planner for time-series forecasting models. By leveraging LLMs' text understanding capabilities, it encodes event descriptions and policy changes as conditioning inputs, enabling time-series foundation models to incorporate textual context into predictions. For example, the model can adjust interest rate forecasts based on the text 'central bank rate hike announcement' without requiring additional training.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2607.24892)

**详情页**：https://ai.daily.yangsir.net/daily/20260730-T0-09

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