Awesome
<div align="center">[IJCAI-24] Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting </div>
Preprint Link (All six datasets [PEMS03, 04, 07, 08, PEMS-BAY, and METR-LA] are included.)
Google Scholar
Due to the modification of STD-MAE's title, you can simply search for "STD-MAE" in Google Scholar to get our article.
Citation
[!NOTE] If you find this repository useful for your research, please cite our work :)
@article{gao2023spatio, title={Spatio-Temporal-Decoupled Masked Pre-training for Traffic Forecasting}, author={Gao, Haotian and Jiang, Renhe and Dong, Zheng and Deng, Jinliang and Song, Xuan}, journal={arXiv preprint arXiv:2312.00516}, year={2023} }
2024 Version
@article{gao2024spatial, title={Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting}, author={Gao, Haotian and Jiang, Renhe and Dong, Zheng and Deng, Jinliang and Ma, Yuxin and Song, Xuan}, journal={arXiv preprint arXiv:2312.00516}, year={2024}, publisher={Apr} }
Performance on Spatiotemporal Forecasting Benchmarks
- Please note you can get a much better performance on PEMS07 dataset using pre-training length of 2016. But it is a time-cosuming operation.
METR-LA | PEMS-BAY |
---|---|
PEMSD7M&L |
---|
You can check more baseline results at Torch-MTS.
💿 Dependencies
OS
Linux systems (e.g. Ubuntu and CentOS).
Python
The code is built based on Python 3.9, PyTorch 1.13.0, and EasyTorch. You can install PyTorch following the instruction in PyTorch.
Miniconda or Anaconda are recommended to create a virtual python environment.
We implement our code based on BasicTS.
Other Dependencies
pip install -r requirements.txt
Getting started
STD-MAE has now been integrated into BasicTS. You can also check many baselines there.
Download Data
You can download data from BasicTS and unzip it.
Preparing Data
- Pre-process Data
You can pre-process all datasets by
cd /path/to/your/project
bash scripts/data_preparation/all.sh
Then the dataset
directory will look like this:
datasets
├─PEMS03
├─PEMS04
├─PEMS07
├─PEMS08
├─raw_data
| ├─PEMS03
| ├─PEMS04
| ├─PEMS07
| ├─PEMS08
├─README.md
Pre-training on S-MAE and T-MAE
cd /path/yourproject
Then run the folloing command to run in Linux screen.
screen -d -m python stdmae/run.py --cfg='stdmae/TMAE_PEMS03.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/TMAE_PEMS04.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/TMAE_PEMS07.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/TMAE_PEMS08.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/SMAE_PEMS03.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/SMAE_PEMS04.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/SMAE_PEMS07.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/SMAE_PEMS08.py' --gpus='0'
Downstream Predictor
After pre-training , copy your pre-trained best checkpoint to mask_save/
.
For example:
cp checkpoints/TMAE_200/064b0e96c042028c0ec44856f9511e4c/TMAE_best_val_MAE.pt mask_save/TMAE_PEMS04_864.pt
cp checkpoints/SMAE_200/50cd1e77146b15f9071b638c04568779/SMAE_best_val_MAE.pt mask_save/SMAE_PEMS04_864.pt
Then run the predictor as :
screen -d -m python stdmae/run.py --cfg='stdmae/STDMAE_PEMS04.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/STDMAE_PEMS03.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/STDMAE_PEMS08.py' --gpus='0'
screen -d -m python stdmae/run.py --cfg='stdmae/STDMAE_PEMS07.py' --gpus='0'
- To find the best result in logs, you can search
best_
in the log files.