from huggingface_hub import hf_hub_download

checkpoint_path = hf_hub_download(
    repo_id="IkeYEUNG/ViTime",
    filename="ViTime_Model.pth",
    revision="v1.0.0",
)
print(checkpoint_path)

Command-line download:

hf download IkeYEUNG/ViTime ViTime_Model.pth --revision v1.0.0

Use with the official GitHub repository

bash

git clone https://github.com/IkeYang/ViTime.git
cd ViTime
python -m pip install -r requirements.txt

The official code downloads this v1.0.0 checkpoint automatically on first use and reuses the Hugging Face cache on later runs:

python

import numpy as np
from main import ViTimePrediction

x = np.sin(np.arange(512) / 10)
model = ViTimePrediction(device="cuda:0", model_name="MAE", lookbackRatio=None)
prediction = model.prediction(x, future_length=720)
print(prediction.shape)

See the GitHub repository for installation requirements and complete point/probabilistic forecasting examples.

Checkpoint security

This release preserves the original PyTorch .pth checkpoint format for compatibility with the official code. Load serialized PyTorch checkpoints only from trusted sources and pin the v1.0.0 revision for reproducible use.

Citation

bibtex

@article{yang2025vitime,
  title={{ViTime}: Foundation Model for Time Series Forecasting Powered by Vision Intelligence},
  author={Yang, Luoxiao and Wang, Yun and Fan, Xinqi and Cohen, Israel and Chen, Jingdong and Zhang, Zijun},
  journal={Transactions on Machine Learning Research},
  year={2025},
  url={https://openreview.net/forum?id=XInsJDBIkp},
  note={Published in Transactions on Machine Learning Research (10/2025)}
}