Upload Graph-WaveNet model trained on PEMS-BAY

README.md

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+---
+tags:
+- traffic-forecasting
+- time-series
+- graph-neural-network
+- graph-wavenet
+datasets:
+- pems-bay
+---
+
+# Graph-WaveNet Model - PEMS-BAY
+
+Graph WaveNet for traffic speed forecasting, combining graph convolution with dilated causal convolution.
+
+## Model Description
+
+This model uses a graph neural network architecture that combines:
+- Adaptive adjacency matrix learning
+- Spatial graph convolution for capturing spatial dependencies
+- Temporal convolution with dilated causal convolutions
+- Multi-scale temporal receptive field
+
+## Evaluation Metrics
+
+- **Test MAE**: 1.5771
+- **Test MAPE**: inf
+- **Test RMSE**: 3.6337
+
+
+## Dataset
+
+**PEMS-BAY**: Traffic speed data from highway sensors.
+
+## Usage
+
+```python
+from utils.gwnet import load_from_hub
+
+# Load model from Hub
+model = load_from_hub("PEMS-BAY")
+
+# Get predictions
+import numpy as np
+x = np.random.randn(10, 12, 207, 2)  # (batch, seq_len, nodes, features)
+predictions = model.predict(x)
+```
+
+## Training
+
+Model was trained using the Graph-WaveNet implementation with default hyperparameters.
+
+## Citation
+
+If you use this model, please cite the original Graph WaveNet paper:
+
+```bibtex
+@inproceedings{wu2019graph,
+  title={Graph WaveNet for Deep Spatial-Temporal Graph Modeling},
+  author={Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},
+  booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
+  pages={1907--1913},
+  year={2019}
+}
+```
+
+## License
+
+This model checkpoint is released under the same license as the training code.

config.json

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+{
+  "num_nodes": 325,
+  "seq_length": 12,
+  "horizon": 12,
+  "input_dim": 2,
+  "output_dim": 1,
+  "nhid": 32,
+  "epochs": 100,
+  "batch_size": 64,
+  "learning_rate": 0.001,
+  "weight_decay": 0.0001,
+  "dropout": 0.3,
+  "device": "cuda",
+  "gcn_bool": true,
+  "addaptadj": true,
+  "adjtype": "doubletransition"
+}

metadata.json

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+{
+  "dataset": "PEMS-BAY",
+  "upload_date": "2025-11-09T16:48:12.130449",
+  "metrics": {
+    "Test MAE": 1.5771052837371826,
+    "Test MAPE": Infinity,
+    "Test RMSE": 3.6337056159973145
+  },
+  "framework": "PyTorch",
+  "model_type": "Graph-WaveNet"
+}

model.pth

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+size 1284851