STGFORMER_CHEB_TCN_XAVIER_DOW_PRETRAIN_NORM_IMPUTE_PEMS-BAY

Spatial-Temporal Graph Transformer (Cheb Tcn Xavier Dow Pretrain Norm Impute) - PEMS-BAY

Spatial-Temporal Graph Transformer (Cheb Tcn Xavier Dow Pretrain Norm Impute) (STGFORMER_CHEB_TCN_XAVIER_DOW_PRETRAIN_NORM_IMPUTE) trained on PEMS-BAY dataset for traffic speed forecasting.

Model Description

Cheb+TCN+Xavier+DOW with full normalized pretraining + imputation

Dataset

PEMS-BAY: Traffic speed data from highway sensors.

Usage

from utils.stgformer import load_from_hub

# Load model from Hub
model, scaler = load_from_hub("PEMS-BAY", hf_repo_prefix="STGFORMER_CHEB_TCN_XAVIER_DOW_PRETRAIN_NORM_IMPUTE")

# Get predictions
from utils.stgformer import get_predictions
predictions = get_predictions(model, scaler, test_dataset)

Training

Model was trained using the STGFORMER_CHEB_TCN_XAVIER_DOW_PRETRAIN_NORM_IMPUTE implementation with default hyperparameters.

Citation

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

@inproceedings{lan2022stgformer,
  title={STGformer: Spatial-Temporal Graph Transformer for Traffic Forecasting},
  author={Lan, Shengnan and Ma, Yong and Huang, Weijia and Wang, Wanwei and Yang, Hui and Li, Peng},
  booktitle={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022}
}

License

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