This code is a PyTorch implementation of our paper "Low-rank Adaptation for Spatio-Temporal Forecasting".
[Highlight] This code is the version as of March 14, 2024, and the updated code will be released upon acceptance of the paper. Part of the information will be hidden during the review phase.
(🌟It's very important for me~~~)
If you find this resource helpful, please consider to star this repository and cite our research:
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python >= 3.7
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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 -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
You can download datasets used in the paper via this link: Google Drive
or use ./download_datasets.sh to download datasets.
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
python main.py --stlora --mlp
# 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
Stay tuned for the latest repo/experiments
Stay tuned for the latest repo/tutorials
The acknowledgment will be hidden during the paper review phase