[ICDE'2023] STWave
Introduction
This is a official PyTorch implementation of the paper: When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks.
Environment
- PyTorch
- fastdtw
- PyWavelets
Data Preparation
PeMSD3 & PeMSD4 & PeMSD7 & PeMSD8
- Download the data PeMSD* with code: p72z.
- Unzip them to corresponding folders.
PeMSD7(M) & PeMSD7(L)
-
Download the data PeMSD7(M).
-
Email authors of STGCN to get the data PeMSD7(L).
Tips
- The name of downloaded datasets should be consistent with the name in config files.
Folder Structure
+-- code-and-data
+-- config # Including detail configurations
+-- cpt # Storing pre-trained weight files (should be created)
+-- data # Including adj files and the main data should be downloaded
+-- lib
| |-- utils.py # Codes of preprocessing datasets and calculating metrics
| |-- graph_utils.py # Codes of calculating eigens and deriving the temporal graph
+-- log # Storing log files (should be created)
+-- model
| |-- models.py # The core source code of our STWave
+-- mian.py # This is the main file for training and testing
+-- README.md # This document
+-- config # Including detail configurations
+-- cpt # Storing pre-trained weight files (should be created)
+-- data # Including adj files and the main data should be downloaded
+-- lib
| |-- utils.py # Codes of preprocessing datasets and calculating metrics
| |-- graph_utils.py # Codes of calculating eigens and deriving the temporal graph
+-- log # Storing log files (should be created)
+-- model
| |-- models.py # The core source code of our STWave
+-- mian.py # This is the main file for training and testing
+-- README.md # This document
Run
Given the example of PeMSD8
mkdir ./cpt/PeMSD8
mkdir ./log/PeMSD8
python main.py --config config/PeMSD8.conf
mkdir ./log/PeMSD8
python main.py --config config/PeMSD8.conf
Citation
If you find our work is helpful, please cite as:
@inproceedings{fang2023spatio,
title={When spatio-temporal meet wavelets: Disentangled traffic forecasting via efficient spectral graph attention networks},
author={Fang, Yuchen and Qin, Yanjun and Luo, Haiyong and Zhao, Fang and Xu, Bingbing and Zeng, Liang and Wang, Chenxing},
booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)},
pages={517--529},
year={2023},
organization={IEEE}
}
Further Reading
- Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective, in SIGKDD 2025. [GitHub Repo]
Authors: Yuchen Fang, Yuxuan Liang, Bo Hui, Zezhi Shao, Liwei Deng, Xu Liu, Xinke Jiang, Kai Zheng.
@article{fang2024efficient,
title={Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective},
author={Fang, Yuchen and Liang, Yuxuan and Hui, Bo and Shao, Zezhi and Deng, Liwei and Liu, Xu and Jiang, Xinke and Zheng, Kai},
journal={arXiv preprint arXiv:2412.09972},
year={2024}
}
title={Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective},
author={Fang, Yuchen and Liang, Yuxuan and Hui, Bo and Shao, Zezhi and Deng, Liwei and Liu, Xu and Jiang, Xinke and Zheng, Kai},
journal={arXiv preprint arXiv:2412.09972},
year={2024}
}
Contributing
We welcome contributions and suggestions!