Official reponsitory for "S^2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting"
Ensure you have installed the necessary dependencies by first building environment:
conda create -n "myenv" python=3.10.0
conda activate myenv
Inside the folder, run:
pip install -r requirements.txt
Begin by downloading the required datasets. All datasets are conveniently available at Autoformer. Create a separate folder named ./data
All scripts are located in ./scripts. Example:
cd Long-term_Forecasting
sh scripts/etth1.shIf you find this repo useful, please consider citing our paper as follows:
@inproceedings{pan2024s,
title={$ S\^{} 2$ IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting},
author={Pan, Zijie and Jiang, Yushan and Garg, Sahil and Schneider, Anderson and Nevmyvaka, Yuriy and Song, Dongjin},
booktitle={Forty-first International Conference on Machine Learning},
year={2024}
}