MTGNN_PEMS-BAY

MTGNN Model - PEMS-BAY

Multivariate Time Series Forecasting with Graph Neural Networks (MTGNN) trained on PEMS-BAY dataset for traffic speed forecasting.

Model Description

This model uses a graph neural network architecture that combines:

• Graph learning to automatically discover spatial dependencies
• Temporal convolution for modeling temporal patterns
• Mix-hop propagation for capturing multi-scale spatial patterns

Evaluation Metrics

• Validation MAE: 1.5766
• Test MAE (15 min): 54.1346
• Test MAPE (15 min): 0.8623
• Test RMSE (15 min): 54.9303

Dataset

PEMS-BAY: Traffic speed data from highway sensors.

Usage

from utils.mtgnn 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, 2, 207, 12)  # (batch, features, nodes, seq_len)
predictions = model.predict(x)

Training

Model was trained using the MTGNN implementation with default hyperparameters.

Citation

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

@inproceedings{wu2020connecting,
  title={Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks},
  author={Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Chang, Xiaojun and Zhang, Chengqi},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
  pages={753--763},
  year={2020}
}

License

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