Jimmy-7664 / STD_MAE

Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting

Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting

💿 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

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

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.

📉 Results table