The official implementation of paper "Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting"
We recommend using the latest versions of dependencies. However, you can refer to the environment.yml file to set up the same environment as we used.
All datasets are stored as CSV files and compressed in GZ format. Please place the datasets in the ./dataset directory.
- For the M5 dataset, we recommend downloading it from M5-methods and preprocessing it using
preprocessing/M5.py. - For other datasets, we recommend downloading them from Autoformer.
All experiments can be reproduced using the scripts/DiPE.sh script.
If you find this repo useful, please cite our paper:
@misc{zhao2024dipe,
title={Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting},
author={Yuang Zhao and Tianyu Li and Jiadong Chen and Shenrong Ye and Fuxin Jiang and Tieying Zhang and Xiaofeng Gao},
year={2024},
eprint={2411.17257},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2411.17257},
}This repo is licensed under the MIT License - see the LICENSE file for details.