---
tags:
- traffic-forecasting
- time-series
- graph-neural-network
- stgformer_internal
datasets:
- pems-bay
---

# Spatial-Temporal Graph Transformer (Internal Implementation) - PEMS-BAY

Spatial-Temporal Graph Transformer (Internal Implementation) (STGFORMER_INTERNAL) trained on PEMS-BAY dataset for traffic speed forecasting.

## Model Description

Baseline STGFormer with learned graph structure



## Dataset

**PEMS-BAY**: Traffic speed data from highway sensors.

## Usage

```python
from utils.stgformer import load_from_hub

# Load model from Hub
model, scaler = load_from_hub("PEMS-BAY", hf_repo_prefix="STGFORMER_INTERNAL")

# Get predictions
from utils.stgformer import get_predictions
predictions = get_predictions(model, scaler, test_dataset)
```

## Training

Model was trained using the STGFORMER_INTERNAL implementation with default hyperparameters.

## Citation

If you use this model, please cite the original STGFORMER_INTERNAL paper:

```bibtex
@inproceedings{lan2022stgformer,
  title={STGformer: Spatial-Temporal Graph Transformer for Traffic Forecasting},
  author={Lan, Shengnan and Ma, Yong and Huang, Weijia and Wang, Wanwei and Yang, Hui and Li, Peng},
  booktitle={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022}
}
```

## License

This model checkpoint is released under the same license as the training code.