VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
<|reference_start|>VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters: Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster. By reformulating TSF as an image reconstruction task, we bridge the gap between image pre-training and TSF downstream tasks. 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 fine-tuning for one epoch, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. Extensive experiments reveal intrinsic similarities between images and real-world time series, suggesting visual models may offer a ``free lunch'' for TSF and highlight the potential for future cross-modality research. Our code is publicly available at https://github.com/Keytoyze/VisionTS.<|reference_end|>
@article{chen2024visionts:,
    title={VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time
  Series Forecasters},
    author={Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang, Jianling Sun,
  Chenghao Liu},
    journal={arXiv preprint arXiv:2408.17253},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17253},
    primaryClass={cs.CV cs.AI cs.LG}
}