| | --- |
| | tags: |
| | - traffic-forecasting |
| | - time-series |
| | - graph-neural-network |
| | - graph-wavenet |
| | datasets: |
| | - metr-la |
| | --- |
| | |
| | # Graph-WaveNet Model - METR-LA |
| |
|
| | 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** (15 min): 2.4840 |
| | - **Test MAPE** (15 min): 0.0626 |
| | - **Test RMSE** (15 min): 4.5781 |
| |
|
| |
|
| | ## Dataset |
| |
|
| | **METR-LA**: Traffic speed data from highway sensors. |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from utils.gwnet import load_from_hub |
| | |
| | # Load model from Hub |
| | model = load_from_hub("METR-LA") |
| | |
| | # 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. |
| |
|
