GWNET_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

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:

@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.