title: VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time
    Series Forecasters
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.
paper_authors._id: 66d58e8c35c36f266f727bf9
paper_authors.name: Mouxiang Chen
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/2566c20d79088ba761215b9a0197cb8e.svg
paper_authors.fullname: Mouxiang Chen
paper_authors.type: user
paper_authors.user: chenmouxiang
paper_authors._id: 66d58e8c35c36f266f727bfa
paper_authors.name: Lefei Shen
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/6d9ea29db1733a7f3a5aa5e534573d7e.svg
paper_authors.fullname: Lefei Shen
paper_authors.type: user
paper_authors.user: HALF111
paper_authors._id: 66d58e8c35c36f266f727bfb
paper_authors.name: Zhuo Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-04T15:25:05.128Z
paper_authors._id: 66bccbe3182d5a69ae73f1c6
paper_authors.avatarUrl: /avatars/8e261a58b61b2664f0646d6f59cd2662.svg
paper_authors.fullname: Zhuo Li
paper_authors.type: user
paper_authors.user: Milanello1998
paper_authors._id: 66d58e8c35c36f266f727bfc
paper_authors.name: Xiaoyun Joy Wang
paper_authors._id: 66d58e8c35c36f266f727bfd
paper_authors.name: Jianling Sun
paper_authors._id: 66d58e8c35c36f266f727bfe
paper_authors.name: Chenghao Liu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-09-02T10:55:25.201Z
paper_authors._id: 64feae72a39388f54fe33d2d
paper_authors.avatarUrl: /avatars/965691697ef2ed2abed274ca51f9d976.svg
paper_authors.fullname: chenghao liu
paper_authors.type: user
paper_authors.user: twinsken
paper_title: VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time
    Series Forecasters
paper_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.
paper_ai_keywords: visual masked autoencoder (MAE) ImageNet zero-shot forecasting