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lstprompt's Introduction

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

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]

lstprompt's People

Contributors

haoxin1998 avatar leozhao1997 avatar

Stargazers

 avatar Hao Li avatar KhaledAlkilane avatar Xuan avatar 陈子昂 avatar  avatar Jindong Wang avatar  avatar  avatar Hua Tang avatar BBD avatar  avatar Антон Зайцев avatar Ruifeng T avatar Attention avatar Ben avatar mh zhang avatar Guancheng Wan avatar 冯思程 avatar Pu Zhang avatar  avatar ShiJZ avatar Shangqing Xu avatar  avatar Liwei Deng avatar GR Yan avatar

Watchers

Alexander Rodríguez avatar Roza Gunes Bayrak avatar

Forkers

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lstprompt's Issues

How do you transform the chat completions to the numerical sequnces?

Hi, I noticed that you have released the code and found that you only modified the 'extra_input' in the gpt.py (Please correct me if I was wrong).When testing on the ETTh1 dataset, I obtained the following completions using your code:

"Sure! Let's start by breaking down the task into short-term and long-term predictions. \n\nShort-term prediction:\nFor short-term predictions, we will focus on trends and the last few steps of the input sequence to forecast the immediate future.\n\nLong-term prediction:\nFor long-term predictions, we will emphasize cyclical patterns and statistical properties of the entire input sequence to forecast further into the future.\n\nLet's begin with short-term predictions:\n\nShort-term Prediction Mechanism:\nTo predict the immediate future, we will use a simple moving average approach that considers the trends in the last few time steps. We will calculate the average of the last 5 time steps and use it as the prediction for the next time step.\n\nShort-term Predictions:\n1. Short-term prediction: 1223\n2. Short-term prediction: 1186\n3. Short-term prediction: 1211\n4. Short-term prediction: 1174\n5. Short-term prediction: 1074\n\nNow, take a deep breath before we move on to long-term predictions.\n\nDeep breath\n\nLong-term Prediction Mechanism:\nFor long-term predictions, we will use a seasonal decomposition of time series (STL) approach to extract the seasonal component and predict future values based on cyclical patterns.\n\nLong-term Predictions:\n1. Long-term prediction: 942\n2. Long-term prediction: 915\n3. Long-term prediction: 890\n4. Long-term prediction: 865\n5. Long-term prediction: 837\n\nDeep breath\n\nI hope these predictions help! Let me know if you need any further assistance."

I am curious about the process you use to transform chat completions into numerical sequences. Additionally, could you share the logs from your code execution? This might help us better understand any potential misunderstandings within the code.

Many thanks!

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