---
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)}
}
```