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witgaw/
STGFORMER_CHEB_TCN_XAVIER_DOW_EXCLUDE_MISSING_K16_PEMS-BAY

Name: STGFORMER_CHEB_TCN_XAVIER_DOW_EXCLUDE_MISSING_K16_PEMS-BAY
Availability: InStock

stgformer_cheb_tcn_xavier_dow_exclude_missing_k16

pems-bay

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Spatial-Temporal Graph Transformer (Cheb Tcn Xavier Dow Exclude Missing K16) - PEMS-BAY

Spatial-Temporal Graph Transformer (Cheb Tcn Xavier Dow Exclude Missing K16) (STGFORMER_CHEB_TCN_XAVIER_DOW_EXCLUDE_MISSING_K16) trained on PEMS-BAY dataset for traffic speed forecasting.

Model Description

STGFormer Chebyshev+TCN with Xavier initialization, DOW embeddings, exclude_missing_from_norm, and sparsity_k=16

Dataset

PEMS-BAY: Traffic speed data from highway sensors.

Usage

python

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_EXCLUDE_MISSING_K16")

# 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_EXCLUDE_MISSING_K16 implementation with default hyperparameters.

Citation

If you use this model, please cite the original STGFORMER_CHEB_TCN_XAVIER_DOW_EXCLUDE_MISSING_K16 paper:

bibtex

@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.

witgaw/STGFORMER_CHEB_TCN_XAVIER_DOW_EXCLUDE_MISSING_K16_PEMS-BAY