Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either fine-tune large language models (LLMs) or build large-scale time-series datasets to develop TSF foundation models. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. In this paper, we explore a new road to building a TSF foundation model from rich and high-quality natural images, based on the intrinsic similarities between images and time series. To bridge the gap between the two domains, we reformulate the TSF task as an image reconstruction task, which is further processed by a visual masked autoencoder (MAE) self-supervised pre-trained on the ImageNet dataset. Surprisingly, without further adaptation in the time-series domain, the proposed VisionTS could achieve superior zero-shot forecasting performance compared to existing TSF foundation models. With minimal fine-tuning, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. These findings suggest that visual models could be a free lunch for TSF and highlight the potential for future cross-domain research between computer vision and TSF. Our code is publicly available at https://github.com/Keytoyze/VisionTS.

本研究针对时间序列预测中的跨域差距和域内异质性问题，提出了一种利用丰富自然图像构建时间序列预测基础模型的新方法。通过将时间序列预测任务再构造为图像重建任务，并使用自监督预训练的视觉掩码自编码器，VisionTS在无进一步适应的情况下展现出优越的零样本预测性能，显示出视觉模型在时间序列预测中的潜力。

VisionTS: 视觉掩码自编码器为零样本时间序列预测提供了免费午餐