https://github.com/adityalab/lstprompt

https://github.com/adityalab/lstprompt

Last synced: 28 days ago
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• Host: GitHub
• URL: https://github.com/adityalab/lstprompt
• Owner: AdityaLab
• License: mit
• Created: 2024-02-21T17:16:55.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-30T19:51:29.000Z (9 months ago)
• Last Synced: 2024-07-31T00:04:46.344Z (9 months ago)
• Language: Jupyter Notebook
• Size: 391 KB
• Stars: 17
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

## Publication

Implementation of the paper "LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting."

Venue: ACL Findings 2024

Authors: Haoxin Liu*, Leo Zhiyuan Zhao*, Jindong Wang, Harshavardhan Kamarthi, B.Aditya Prakash

Paper + Appendix: [https://arxiv.org/abs/2402.16132](https://arxiv.org/abs/2402.16132)

## Run LSTPrompt

Try LSTPrompt through the quick demo `demo.ipynb` notebook.

## Contact

If you have any questions about the code, please contact:

Haoxin Liu at ```hliu763[at]gatech[dot]edu```;

Leo Zhiyuan Zhao at  ```leozhao1997[at]gatech[dot]edu```.

## Citation

If you find our work useful, please cite our work:

```

@article{liu2024lstprompt,

  title={LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting},

  author={Liu, Haoxin and Zhao, Zhiyuan and Wang, Jindong and Kamarthi, Harshavardhan and Prakash, B Aditya},

  journal={arXiv preprint arXiv:2402.16132},

  year={2024}

}

```

## Acknowledgement

This repo is constructed based on the following repos:

[https://github.com/ngruver/llmtime]