---
tags:
- traffic-forecasting
- time-series
- graph-neural-network
- transformer
- stgformer
datasets:
- metr-la
---

# STGformer Model - METR-LA

Spatio-Temporal Graph Transformer (STGformer) trained on METR-LA dataset for traffic speed forecasting.

## Model Description

This model uses a transformer-based graph neural network architecture that combines:
- Self-attention mechanisms for capturing temporal dependencies
- Spatial graph convolution for modeling spatial relationships
- Adaptive embeddings for learning node-specific patterns
- Time-of-day embeddings for capturing daily patterns

## Evaluation Metrics

- **Test MAE (15 min)**: 2.5637
- **Test MAPE (15 min)**: 0.0654
- **Test RMSE (15 min)**: 4.8755


## Dataset

**METR-LA**: Traffic speed data from highway sensors.

## Usage

```python
from utils.stgformer import load_from_hub

# Load model from Hub
model, scaler = load_from_hub("METR-LA")

# Get predictions
import numpy as np
x = np.random.randn(10, 12, 207, 2)  # (batch, seq_len, nodes, [value, tod])
predictions = model.predict(x)
```

## Training

Model was trained using the STGformer implementation with configuration:
- Input features: 2 [speed, time-of-day]
- Time-of-day embedding dimension: 24
- Day-of-week embedding dimension: 0 (disabled)
- Adaptive embedding dimension: 80
- Number of attention heads: 4
- Number of layers: 3

## Citation

If you use this model, please cite the STGformer paper:

```bibtex
@article{stgformer,
  title={STGformer: Spatio-Temporal Graph Transformer for Traffic Forecasting},
  author={Author names},
  journal={Conference/Journal},
  year={Year}
}
```

## License

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