Upload Graph-WaveNet model trained on METR-LA
Browse files- README.md +68 -0
- config.json +17 -0
- metadata.json +11 -0
- model.pth +3 -0
README.md
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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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- graph-wavenet
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datasets:
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- metr-la
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---
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# Graph-WaveNet Model - METR-LA
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Graph WaveNet for traffic speed forecasting, combining graph convolution with dilated causal convolution.
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## Model Description
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This model uses a graph neural network architecture that combines:
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- Adaptive adjacency matrix learning
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- Spatial graph convolution for capturing spatial dependencies
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- Temporal convolution with dilated causal convolutions
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- Multi-scale temporal receptive field
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## Evaluation Metrics
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- **Test MAE**: 9.9133
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- **Test MAPE**: inf
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- **Test RMSE**: 21.8977
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## Dataset
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**METR-LA**: Traffic speed data from highway sensors.
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## Usage
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```python
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from utils.gwnet import load_from_hub
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# Load model from Hub
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model = load_from_hub("METR-LA")
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# Get predictions
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import numpy as np
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x = np.random.randn(10, 12, 207, 2) # (batch, seq_len, nodes, features)
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predictions = model.predict(x)
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```
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## Training
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Model was trained using the Graph-WaveNet implementation with default hyperparameters.
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## Citation
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If you use this model, please cite the original Graph WaveNet paper:
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```bibtex
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@inproceedings{wu2019graph,
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title={Graph WaveNet for Deep Spatial-Temporal Graph Modeling},
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author={Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},
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booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
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pages={1907--1913},
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year={2019}
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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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config.json
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{
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"num_nodes": 207,
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"seq_length": 12,
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"horizon": 12,
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"input_dim": 2,
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"output_dim": 1,
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"nhid": 32,
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"epochs": 100,
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"batch_size": 64,
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"learning_rate": 0.001,
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"weight_decay": 0.0001,
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"dropout": 0.3,
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"device": "cuda",
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"gcn_bool": true,
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"addaptadj": true,
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"adjtype": "doubletransition"
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}
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metadata.json
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{
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"dataset": "METR-LA",
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"upload_date": "2025-11-09T15:16:28.728700",
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"metrics": {
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"Test MAE": 9.91329574584961,
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"Test MAPE": Infinity,
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"Test RMSE": 21.897747039794922
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},
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"framework": "PyTorch",
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"model_type": "Graph-WaveNet"
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}
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model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:9f2f83990a91d83e1a7657b38bae00c15935c9ab68b0f5e979fc5b88f35e7089
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size 1275379
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