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.

本研究针对现有时间序列推理方法的不足，提出了一种新颖的多模态时间序列大语言模型（MLLM）方法，旨在实现跨领域的可泛化信息学习。通过引入轻量级时间序列编码器和链式思维增强任务，我们的模型在零样本推理任务中表现优于GPT-4o，展示了在时间序列特点提取和思维路径生成上的显著进步。

面向时间序列推理的多模态大语言模型研究