用视觉语言模型做时间序列分类，微调两轮就有效。
On the Feasibility of Vision-Language Models for Time-Series Classification
- 将时序数据转为图像，结合数值与图形信息输入VLM。
- 微调两轮内达到可比性能，支持单变量和多变量数据。
- 适合想用大模型做时序分析的研究者和工程师。
我们基于视觉语言模型（VLMs）开展时间序列分类研究。发现对VLM进行两轮或更少的微调即可获得具有竞争力的结果。提出一种新方法，将图形化数据表示作为图像与数值数据一同输入，基于假设：图形信息能提供数值数据无法捕捉的上下文。此外，图形表示可规避大语言模型常见的上下文长度限制。为推进该工作，我们构建了一个可扩展的端到端训练流程，可用于不同场景，从而分离出从大语言模型向时间序列分类任务迁移的最佳策略。该方法适用于单变量和多变量时间序列数据。通过大量且实用的实验，验证了该方法在时间序列分类及生成标签任务中的有效性。
原文摘要 · Abstract (English)
We build upon time-series classification by leveraging the capabilities of Vision Language Models (VLMs). We find that VLMs produce competitive results after two or less epochs of fine-tuning. We develop a novel approach that incorporates graphical data representations as images in conjunction with numerical data. This approach is rooted in the hypothesis that graphical representations can provide additional contextual information that numerical data alone may not capture. Additionally, providing a graphical representation can circumvent issues such as limited context length faced by LLMs. To further advance this work, we implemented a scalable end-to-end pipeline for training on different scenarios, allowing us to isolate the most effective strategies for transferring learning capabilities from LLMs to Time Series Classification (TSC) tasks. Our approach works with univariate and multivariate time-series data. In addition, we conduct extensive and practical experiments to show how this approach works for time-series classification and generative labels.
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