Summary of Visionts: Visual Masked Autoencoders Are Free-lunch Zero-shot Time Series Forecasters, by Mouxiang Chen et al.
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
by Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang, Jianling Sun, Chenghao Liu
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores a novel approach to building time series forecasting (TSF) foundation models by leveraging rich, high-quality natural images. The proposed VisionTS model is pre-trained on the ImageNet dataset and reformulates TSF as an image reconstruction task, effectively bridging the gap between image pre-training and TSF downstream tasks. Without further adaptation in the time series domain, VisionTS achieves better zero-shot forecast performance than existing TSF foundation models. With fine-tuning for one epoch, VisionTS can further improve forecasting and achieve state-of-the-art performance in most cases. The paper’s findings suggest intrinsic similarities between images and real-world time series, implying a potential “free lunch” for TSF and highlighting opportunities for future cross-modality research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using pictures to help predict the future of things like stock prices or weather. It’s a new way of doing something that usually requires lots of data and special training. The idea is that if you can teach a computer to recognize patterns in pictures, it might be able to use those same patterns to forecast what will happen next with time series data. Surprisingly, this approach works really well without needing any extra training! The paper’s results show that this method can even outperform other methods that have been tried before. |
Keywords
» Artificial intelligence » Fine tuning » Time series » Zero shot
