让大模型学会用自然语言推理时间序列数据，效果超越GPT-4o。
Towards Time Series Reasoning with LLMs
- 用轻量编码器直接提取时序特征，接入大模型进行联合训练。
- 在零样本任务中表现优于GPT-4o，能识别斜率、频率等关键特征。
- 适合需要自然语言解释时间趋势的研究者与开发者。
多模态大语言模型在视觉等领域已取得显著进展，但在时间序列领域尚未实现同等突破。尽管已有研究在时间序列预测上展现潜力，但极少工作探索大模型对时间序列的自然语言推理能力。本文提出一种新型多模态时间序列大模型方法，通过在大模型上训练轻量级时间序列编码器，直接提取时序信息，并采用思维链增强的任务进行微调，促使模型生成推理路径。实验表明，该模型学习到反映特定时序特征（如斜率、频率）的隐表示，在多个领域的零样本推理任务中性能超越GPT-4o。
原文摘要 · Abstract (English)
Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show how an LLM could be used for time-series reasoning in natural language. We propose a novel multi-modal time-series LLM approach that learns generalizable information across various domains with powerful zero-shot performance. First, we train a lightweight time-series encoder on top of an LLM to directly extract time-series information. Then, we fine-tune our model with chain-of-thought augmented time-series tasks to encourage the model to generate reasoning paths. We show that our model learns a latent representation that reflects specific time-series features (e.g. slope, frequency), as well as outperforming GPT-4o on a set of zero-shot reasoning tasks on a variety of domains.
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