https://github.com/XiongxiaoXu/Mambaformer-in-Time-Series

The official implementation of the paper: "SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting"
https://github.com/XiongxiaoXu/Mambaformer-in-Time-Series

Last synced: 4 months ago
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The official implementation of the paper: "SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting"

• Host: GitHub
• URL: https://github.com/XiongxiaoXu/Mambaformer-in-Time-Series
• Owner: XiongxiaoXu
• Created: 2024-04-18T21:26:28.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-11-05T04:22:07.000Z (4 months ago)
• Last Synced: 2024-11-05T04:27:37.566Z (4 months ago)
• Language: Python
• Homepage: https://arxiv.org/abs/2404.14757
• Size: 17.5 MB
• Stars: 117
• Watchers: 4
• Forks: 6
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-state-space-models - GitHub

README

        
# SST

The SST (State Space Transformer) code for the paper "[SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting](https://arxiv.org/abs/2404.14757)".



## Contributions

* We propose to **decompose time series into global patterns and local variations according to ranges**. We identify that global patterns as the focus of long range and local variations should be captured in short range.

* To effectively capture long-term patterns and short-term variations, we leverage the patching to create coarser PTS in long range and finer PTS in short range. Moreover, we introduce **a new metric to precisely quantify the resolution of PTS**.

* We propose a **novel hybrid Mamba-Transformer experts architecture SST**, with Mamba as a global patterns expert in long range, and LWT as a local variations expert in short range. A long-short router is designed to adaptively integrate the global patterns and local variations. **With Mamba and LWT, SST is highly scalable with linear complexity O(L) on time series length L**.

## Getting Started

### Environment

* python            3.10.13

* torch             1.12.1+cu116

* mamba-ssm         1.2.0.post1

* numpy             1.26.4

* transformers      4.38.2

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](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy)" 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:

```bibtex

@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}, 

}

```