Kavli Affiliate: Zhuo Li
| First 5 Authors: Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang, Jianling Sun
| Summary:
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.
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