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README.md

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-readme
+<div align="center">
+  <!-- <h1><b> Time-LLM </b></h1> -->
+  <!-- <h2><b> Time-LLM </b></h2> -->
+  <h2><b> (IJCAI'25) RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming </b></h2>
+</div>
+
+
+
+
+
+
+---
+>
+> 🙋 Please let us know if you find out a mistake or have any suggestions!
+> 
+> 🌟 If you find this resource helpful, please consider to star this repository and cite our research:
+
+```
+@inproceedings{wang2025repst,
+  title={RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming},
+  author={Wang, Hao and Han, Jindong and Fan, Wei and Sun, Leilei and Liu, Hao},
+  booktitle={Proceedings of the 34th International Joint Conference on Artificial Intelligence},
+  year={2025}
+}
+```
+
+
+## Introduction
+This repository contains the implementation of REPST, a framework for spatio-temporal forecasting that leverages the reasoning and generalization capabilities of Pre-trained Language Models (PLMs). REPST utilizes a semantic-aware spatio-temporal decomposer and selective discrete reprogramming to enable PLMs to handle complex spatio-temporal data, especially in data-scarce environments.
+
+<p align="center">
+<img src="./figures/repst.png" height = "360" alt="" align=center />
+</p>
+
+- RePST comprises two key components: (1) a dynamic mode decomposition approach that disentangles spatially correlated time series into interpretable components, and (2) an expanded spatio-temporal vocabulary that helps PLMs better understand the dynamics of complex spatio-temporal systems, to guide PLM reasoning.
+
+<p align="center">
+<img src="./figures/method-detailed-illustration.png" height = "190" alt="" align=center />
+</p>
+
+## Requirements
+Use python 3.11 from MiniConda
+
+- torch==2.2.2
+- accelerate==0.28.0
+- einops==0.7.0
+- matplotlib==3.7.0
+- numpy==1.23.5
+- pandas==1.5.3
+- scikit_learn==1.2.2
+- scipy==1.12.0
+- tqdm==4.65.0
+- peft==0.4.0
+- transformers==4.31.0
+- deepspeed==0.14.0
+- sentencepiece==0.2.0
+
+To install all dependencies:
+```
+pip install -r requirements.txt
+```
+
+## Datasets
+# Pending
+You can access the well pre-processed datasets from [[Google Drive]](https://drive.google.com/), then place the downloaded contents under `./dataset`
+
+
+
+## Detailed usage
+
+Please refer to ```run.py``` for the detailed description of each hyperparameter.
+
+
+
+
+## Acknowledgement
+Our baseline model implementation adapts [BasicTS](https://github.com/GestaltCogTeam/BasicTS) as the code base and have extensively modified it to our purposes. We thank the authors for sharing their implementations and related resources.