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--- |
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tags: |
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- traffic-forecasting |
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- time-series |
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- graph-neural-network |
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- stgformer_pretrained |
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datasets: |
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- largest-gla |
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--- |
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# Spatial-Temporal Graph Transformer (Pretrained) - LARGEST-GLA |
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Spatial-Temporal Graph Transformer (Pretrained) (STGFORMER_PRETRAINED) trained on LARGEST-GLA dataset for traffic speed forecasting. |
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## Model Description |
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STGFormer pretrained checkpoint for LARGEST-GLA. This checkpoint contains pretrained model weights and imputation head from masked node pretraining. Use with load_from config option. |
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## Dataset |
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**LARGEST-GLA**: Traffic speed data from highway sensors. |
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## Usage |
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```python |
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from utils.stgformer import load_from_hub |
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# Load model from Hub |
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model, scaler = load_from_hub("LARGEST-GLA", hf_repo_prefix="STGFORMER_PRETRAINED") |
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# Get predictions |
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from utils.stgformer import get_predictions |
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predictions = get_predictions(model, scaler, test_dataset) |
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``` |
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## Training |
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Model was trained using the STGFORMER_PRETRAINED implementation with default hyperparameters. |
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## Citation |
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If you use this model, please cite the original STGFORMER_PRETRAINED paper: |
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```bibtex |
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@inproceedings{lan2022stgformer, |
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title={STGformer: Spatial-Temporal Graph Transformer for Traffic Forecasting}, |
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author={Lan, Shengnan and Ma, Yong and Huang, Weijia and Wang, Wanwei and Yang, Hui and Li, Peng}, |
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booktitle={IEEE Transactions on Neural Networks and Learning Systems}, |
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year={2022} |
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} |
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``` |
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## License |
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This model checkpoint is released under the same license as the training code. |
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