This Official repository contains PyTorch codes for CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting paper.

🌟 If you find this resource helpful, please consider to star this repository and cite our research:

@inproceedings{xue2024card,
  title={CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting},
  author={Xue, Wang and Zhou, Tian and Wen, QingSong and Gao, Jinyang and Ding, Bolin and Jin, Rong},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2024}
}

In case of any questions, bugs, suggestions or improvements, please feel free to open an issue.

Channel Alignment: Allow information to be shared among different channels/covariates.

Dual Attention: Explore the within-patch information.

Token Blend: Utilize mutli-scale knowledge.

1. Dataset can be obtained from Time Series Library (TSlib) at https://github.com/thuml/Time-Series-Library/tree/main

2. The code for long-term forecasting experiment in section 5.1 is in folder long_term_forecast_l96. We provide the experiment scripts of all benchmarks under the folder long_term_forecast_l96/scripts/CARD. You can reproduce the multivariate experiments by running the following shell scripts:

cd long_term_forecast_l96
bash scripts/CARD/ETT.sh 
bash scripts/CARD/wEATHER.sh 
bash scripts/CARD/ECL.sh 
bash scripts/CARD/Traffic.sh 

3. The code for long-term forecasting experiment in Appendix E is in folder long_term_forecast_l720. We provide the experiment scripts of all benchmarks under the folder long_term_forecast_l720/scripts/CARD. You can reproduce the multivariate experiments by running the following shell scripts:

cd long_term_forecast_l720
bash scripts/CARD/ettm1.sh
bash scripts/CARD/ettm2.sh
bash scripts/CARD/etth1.sh
bash scripts/CARD/etth2.sh
bash scripts/CARD/weather.sh
bash scripts/CARD/electricity.sh
bash scripts/CARD/traffic.sh

4. The code for short-term M4 forecasting experiment in section 5.2 is in folder short_term_forecast_m4. We provide the experiment scripts of all benchmarks under the folder short_term_forecast_m4/scripts/CARD. You can reproduce the multivariate experiments by running the following shell scripts:

cd short_term_forecast_m4
bash scripts/CARD_M4.sh 

We appreciate the following github repo very much for the valuable code base:

https://github.com/yuqinie98/PatchTST https://github.com/thuml/Time-Series-Library

If you have any questions or concerns, please contact us: [email protected] or [email protected]

1, Transformers in Time Series: A Survey, in IJCAI 2023. [GitHub Repo]

@inproceedings{wen2023transformers,
  title={Transformers in time series: A survey},
  author={Wen, Qingsong and Zhou, Tian and Zhang, Chaoli and Chen, Weiqi and Ma, Ziqing and Yan, Junchi and Sun, Liang},
  booktitle={International Joint Conference on Artificial Intelligence(IJCAI)},
  year={2023}
}