In-Context Inpainting for Time Series Forecasting

2026-08-24

AI Summary

ICI-Time通过将时间序列转换为视觉表示，并利用大型视觉模型进行图像修复，解决了时间序列预测问题，无需专门的时序架构或大量训练。

Abstract

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.

Problem

• time series forecasting
• visual inpainting
• large vision models

Innovation

• In-Context Inpainting
• Time Series Forecasting
• Large Vision Models
• Visual Representations
• Pattern Completion