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 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.
66d58e8c35c36f266f727bf9
Mouxiang Chen
admin_assigned
2024-09-03T07:47:41.241Z
66d58df54b87a685ccb8e4a0
/avatars/2566c20d79088ba761215b9a0197cb8e.svg
Mouxiang Chen
user
chenmouxiang
66d58e8c35c36f266f727bfa
Lefei Shen
admin_assigned
2024-09-03T08:28:01.595Z
6609227c57b373706917a4ef
/avatars/6d9ea29db1733a7f3a5aa5e534573d7e.svg
Lefei Shen
user
HALF111
66d58e8c35c36f266f727bfb
Zhuo Li
claimed_verified
2024-09-04T15:25:05.128Z
66bccbe3182d5a69ae73f1c6
/avatars/8e261a58b61b2664f0646d6f59cd2662.svg
Zhuo Li
user
Milanello1998
66d58e8c35c36f266f727bfc
Xiaoyun Joy Wang
66d58e8c35c36f266f727bfd
Jianling Sun
66d58e8c35c36f266f727bfe
Chenghao Liu
extracted_confirmed
2024-09-02T10:55:25.201Z
64feae72a39388f54fe33d2d
/avatars/965691697ef2ed2abed274ca51f9d976.svg
chenghao liu
user
twinsken
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 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.
The VisionTS model, leveraging a visual masked autoencoder pre-trained on ImageNet, achieves state-of-the-art zero-shot and fine-tuned forecasting performance in time series forecasting without adapting specifically to the time-series domain.
Foundation models time series forecasting (TSF) large language models (LLMs) large-scale time-series datasets visual masked autoencoder (MAE) ImageNet VisionTS