| --- |
| tags: |
| - traffic-forecasting |
| - time-series |
| - graph-neural-network |
| - abl_no_dow |
| datasets: |
| - pems-bay |
| --- |
| |
| # Spatial-Temporal Graph Transformer (Ablation: No Dow) - PEMS-BAY |
|
|
| Spatial-Temporal Graph Transformer (Ablation: No Dow) (ABL_NO_DOW) trained on PEMS-BAY dataset for traffic speed forecasting. |
|
|
| ## Model Description |
|
|
| Ablation: Full model WITHOUT DOW embeddings (Cheb+TCN+Xavier+ExcludeMissing+K16) |
|
|
|
|
|
|
| ## 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="ABL_NO_DOW") |
| |
| # Get predictions |
| from utils.stgformer import get_predictions |
| predictions = get_predictions(model, scaler, test_dataset) |
| ``` |
|
|
| ## Training |
|
|
| Model was trained using the ABL_NO_DOW implementation with default hyperparameters. |
|
|
| ## Citation |
|
|
| If you use this model, please cite the original ABL_NO_DOW 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. |
|
|
