This code is a PyTorch implementation of our paper "Low-rank Adaptation for Spatio-Temporal Forecasting".
[Highlight] The updated code will be released with more baselines and modules upon acceptance of the paper. Part of the information will be hidden during the review phase. The latest source code will be released when the paper is accepted.
(🌟It's very important for me~~~)
If you find this resource helpful, please consider to star this repository and cite our research:
- python >= 3.7
- torch==1.13.1
All dependencies can be installed using the following command:
conda create -n stlora python==3.7
conda activate stlora
pip install torch==1.13.1 torch_geometric torchinfo
pip install -r requirements.txt
- main.py
- data
- generate_training_data -> refer to 'Graph-WaveNet'
- rawdata.h5 -> year_dataset/(his.npz, idx_test.npy, idx_train.npywe4, idx_val.npy)
- experiments -> expr. log
- save -> model / results
- src -> source code for stlora
Our experiments utilize six public traffic datasets: PEMS03, PEMS04, PEMS07, PEMS08, METR-LA, and PEMS-BAY. You can download these datasets via:
- Option 1: Google Drive
- Option 2: Run
./download_datasets.sh
| Dataset | #Nodes | #Frames | Time Range |
|---|---|---|---|
| METR-LA | 207 | 34,272 | 03/2012 – 06/2012 |
| PEMS-BAY | 325 | 52,116 | 01/2017 – 06/2017 |
| PEMS03 | 358 | 26,208 | 09/2018 – 11/2018 |
| PEMS04 | 307 | 16,992 | 01/2018 – 02/2018 |
| PEMS07 | 883 | 28,224 | 05/2017 – 08/2017 |
| PEMS08 | 170 | 17,856 | 07/2016 – 08/2016 |
python main.py [-dataset] [-device] [-pre_train] [-seed] [-epochs] ...
Examples for all parameters in commands. You can modify some of the default parameters in ./src/utils/args.py contained:
--seed=998244353
--batch_size=64
--seq_length=12
--horizon=12
--input_dim=3
--output_dim=1
--mode=train
It is also recommended that you train with the following commands and modifiable parameters:
python main.py --device=cuda:1 --dataset=PEMS08 --years=2016 --stlora
# using python main.py to train original models
# You need to modify the backbone model in the `main.py` header file
# original model
python main.py --device=cuda:1 --dataset=PEMS04 --years=2018 --mode=train
# use st-lora for adjustment
python main.py --mode=train --stlora --mlp --num_nalls=4 --embed_dim=24 --num_mlrfs=4
# Enhance STGCN / GWN / AGCRN / STID, please Uncomment in main.py
python main.py --device=cuda:0 --dataset=PEMS04 --years=2018 --mode=train --stlora --num_nalls=4 --embed_dim=24
python main.py --device=cuda:0 --dataset=PEMS03 --years=2018 --mode=train --stlora --num_nalls=4 --embed_dim=24
python main.py --device=cuda:0 --dataset=PEMS07 --years=2017 --mode=train --stlora --num_nalls=4 --embed_dim=24
python main.py --device=cuda:0 --dataset=PEMS08 --years=2016 --mode=train --stlora --num_nalls=4 --embed_dim=24
Stay tuned for the latest repo/experiments Our experiments show significant improvements across multiple metrics (MAE, RMSE, MAPE) when applying ST-LoRA to backbone models. For detailed results, please refer to our paper.
# please Uncomment and give model_name at the same time
python main.py --device=cuda:0 --dataset=PEMS08 --years=2016 --mode=train --stlora --num_nalls=4 --embed_dim=24 --pre_train --load_pretrain_path='best.pt' --model_name=AGCRN
Stay tuned for additional visualization examples in the tutorials/ directory.
The acknowledgment will be hidden during the paper review phase