把时间序列预测变成视觉补全任务，用现成的视觉模型直接推理。
In-Context Inpainting for Time Series Forecasting
- 将时间序列转为面积图，用视觉模型在上下文里补全模式。
- 在流行病、气象和电力数据上表现媲美深度学习模型。
- 无需微调或改架构，适合数据少的场景，适合跨领域应用。
我们提出 ICI-Time，一种将时间序列预测重构为视觉补全任务的新框架，利用大视觉模型（LVMs）的泛化能力。与需要专用时序结构和大量领域训练的方法不同，ICI-Time 将时间序列转换为结构化视觉表示（面积图），并应用视觉上下文学习，将预测重构为网格提示中的模式补全问题，可由预训练视觉变压器在不进行微调或架构修改的情况下求解。时间依赖关系通过空间布局表示，数值域与视觉域之间存在一致且可逆的映射。在流行病学、气象学和电力系统上的广泛实验表明，ICI-Time 在性能上可与深度学习基线竞争，并在数据有限情况下展现出良好适应性，引入了一种连接时序与视觉领域的全新范式。
原文摘要 · Abstract (English)
We propose ICI-Time, a novel framework that reframes time series forecasting as a visual inpainting task, leveraging the generalisation power of large vision models (LVMs). Unlike methods that require specialised temporal architectures and extensive domain-specific training, ICI-Time transforms time series into structured visual representations (area charts) and applies visual in-context learning, reformulating forecasting as pattern completion within a grid-structured prompt that pre-trained vision transformers can solve without fine-tuning or architectural modification. Temporal dependencies are represented through spatial layout, with a consistent, invertible mapping between numerical and visual domains. Extensive experiments across epidemiology, meteorology, and power systems demonstrate that ICI-Time performs competitively against deep learning baselines and shows promising adaptability under limited-data settings, introducing a new paradigm that bridges temporal and visual domains.
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