VCformer (IJCAI 2024)

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The repo is the official implementation for the paper: VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

Overall Architecture

The pseudo-code of VCformer is as simple as the following:

Usage

1. Install Python 3.8. For convenience, execute the following command.

   pip install -r requirements.txt 

2. Prepare data. You can obtain the well pre-processed datasets from [Google Drive] or [Baidu Drive], Then place the downloaded data in the folder./dataset. Here is a summary of used datasets.

3. Train and evaluate model. We provide the experiment scripts for all benchmarks under the folder ./scripts/. You can reproduce the experiment results as the following examples:

   sh ./scripts/Traffic/VCformer.sh

Citation

If you want to cite our paper, use the citation below:

@inproceedings{ijcai2024p590,
  title     = {VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting},
  author    = {Yang, Yingnan and Zhu, Qingling and Chen, Jianyong},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {5335--5343},
  year      = {2024},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2024/590},
  url       = {https://doi.org/10.24963/ijcai.2024/590},
}

Acknowledgement

We appreciate the following Github repos a lot for their valuable code and efforts.

• Time-Series-Library (https://github.com/thuml/Time-Series-Library)
• PatchTST (https://github.com/yuqinie98/PatchTST)
• Crossformer (https://github.com/Thinklab-SJTU/Crossformer)