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

# Graph-WaveNet Model - PEMS-BAY

Graph WaveNet for traffic speed forecasting, combining graph convolution with dilated causal convolution.

## Model Description

This model uses a graph neural network architecture that combines:
- Adaptive adjacency matrix learning
- Spatial graph convolution for capturing spatial dependencies
- Temporal convolution with dilated causal convolutions
- Multi-scale temporal receptive field

## Evaluation Metrics

- **Test MAE (15 min)**: 1.0924
- **Test MAPE (15 min)**: 0.0222
- **Test RMSE (15 min)**: 2.2119


## Dataset

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

## Usage

```python
from utils.gwnet import load_from_hub

# Load model from Hub
model = load_from_hub("PEMS-BAY")

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

## Training

Model was trained using the Graph-WaveNet implementation with default hyperparameters.

## Citation

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

```bibtex
@inproceedings{wu2019graph,
  title={Graph WaveNet for Deep Spatial-Temporal Graph Modeling},
  author={Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},
  booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
  pages={1907--1913},
  year={2019}
}
```

## License

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