TimeDRL

Welcome to the official codebase of TimeDRL.
This project is based on research that has been accepted for publication at the International Conference on Data Engineering (ICDE) 2024.

Usage

1. Install Python 3.8, and use requirements.txt to install the dependencies

   pip install -r requirements.txt
   

2. To execute the script with configuration settings passed via argparse, use:

   python main.py --...
   

   Alternatively, if you prefer to use locally defined parameters to overwrite args for faster experimentation iterations, run:

   python main.py --overwrite_args
   

3. Please refer to exp_settings_and_results to see all the experiments' settings and corresponding results.

Citation

If you find value in this repository, we kindly ask that you cite our paper.

@article{chang2023timedrl,
  title={TimeDRL: Disentangled Representation Learning for Multivariate Time-Series},
  author={Chang, Ching and Chan, Chiao-Tung and Wang, Wei-Yao and Peng, Wen-Chih and Chen, Tien-Fu},
  journal={arXiv preprint arXiv:2312.04142},
  year={2023}
}

Contact

If you have any questions or suggestions, please reach out to Ching Chang at [email protected], or raise them in the 'Issues' section.

Acknowledgement

This library was built upon the following repositories:

• Time Series Library (TSlib): https://github.com/thuml/Time-Series-Library