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license: apache-2.0
library_name: pytorch
pipeline_tag: time-series-forecasting
tags:
- time-series
- time-series-forecasting
- zero-shot
- probabilistic-forecasting
- vision
---
# ViTime
Official pretrained checkpoint for **ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence**.
- Code: https://github.com/IkeYang/ViTime
- Paper: https://openreview.net/forum?id=XInsJDBIkp
ViTime converts numerical time series into binary images and performs point and probabilistic forecasting with a vision-based architecture.
## Checkpoint
| File | Size | SHA-256 |
| --- | ---: | --- |
| `ViTime_Model.pth` | 296,778,986 bytes | `6513b03b352163337c333526fd3634b07db789665cd9f87648f9661efe0cff1a` |
The stable release revision is `v1.0.0`.
## Download
Public downloads do not require a Hugging Face account or access token.
```python
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:
```bash
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)}
}
```
|