(IJCAI'25) RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming
---
>
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```
@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.
- 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.
## 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.