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---
tags:
- traffic-forecasting
- time-series
- graph-neural-network
- graph-wavenet
datasets:
- metr-la
---
# Graph-WaveNet Model - METR-LA
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)**: 2.4767
- **Test MAPE (15 min)**: 0.0618
- **Test RMSE (15 min)**: 4.5875
## Dataset
**METR-LA**: Traffic speed data from highway sensors.
## Usage
```python
from utils.gwnet import load_from_hub
# Load model from Hub
model = load_from_hub("METR-LA")
# 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.
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