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SST

The SST (State Space Transformer) code for the paper "SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting".

<img width="1075" alt="image" src="https://github.com/user-attachments/assets/93128514-7ada-4f3e-9c5e-3fad8bde8ae1">

Contributions

Getting Started

Environment

The installation of mamba-ssm package can refer to https://github.com/state-spaces/mamba.

Run

To run SST on various dataset, run corrrsponidng dataset .sh files in the scripts folder.

For exmaple, run SST on the Weather dataset: ./weather.sh

Dataset

You can download all the datasets from the "Autoformer" project. Creatae a dataset folder in the current directory and put the downloaded datasets into dataset folder.

Acknowledgement

We would like to greatly thank the following awesome projects:

Mamba (https://github.com/state-spaces/mamba)

PatchTST (https://github.com/yuqinie98/PatchTST)

LTSF-Linear (https://github.com/cure-lab/LTSF-Linear)

Autoformer (https://github.com/thuml/Autoformer)

Cite

If you find this repository useful for your work, please consider citing the paper as follows (bib format from arxiv):

@misc{xu2024sstmultiscalehybridmambatransformer,
      title={SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting}, 
      author={Xiongxiao Xu and Canyu Chen and Yueqing Liang and Baixiang Huang and Guangji Bai and Liang Zhao and Kai Shu},
      year={2024},
      eprint={2404.14757},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2404.14757}, 
}