metadata
tags:
- traffic-forecasting
- time-series
- graph-neural-network
- abl_no_graph
datasets:
- pems-bay
Spatial-Temporal Graph Transformer (Ablation: No Graph) - PEMS-BAY
Spatial-Temporal Graph Transformer (Ablation: No Graph) (ABL_NO_GRAPH) trained on PEMS-BAY dataset for traffic speed forecasting.
Model Description
Ablation: Full model WITHOUT graph propagation (Cheb+TCN+Xavier+DOW+ExcludeMissing, GraphMode=NONE)
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="ABL_NO_GRAPH")
# Get predictions
from utils.stgformer import get_predictions
predictions = get_predictions(model, scaler, test_dataset)
Training
Model was trained using the ABL_NO_GRAPH implementation with default hyperparameters.
Citation
If you use this model, please cite the original ABL_NO_GRAPH 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.