76 lines
2.5 KiB
Markdown
76 lines
2.5 KiB
Markdown
<div align="center">
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<!-- <h1><b> Time-LLM </b></h1> -->
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<!-- <h2><b> Time-LLM </b></h2> -->
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<h2><b> (IJCAI'25) RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming </b></h2>
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</div>
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---
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>
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> 🙋 Please let us know if you find out a mistake or have any suggestions!
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>
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> 🌟 If you find this resource helpful, please consider to star this repository and cite our research:
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```
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@inproceedings{wang2025repst,
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title={RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming},
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author={Wang, Hao and Han, Jindong and Fan, Wei and Sun, Leilei and Liu, Hao},
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booktitle={Proceedings of the 34th International Joint Conference on Artificial Intelligence},
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year={2025}
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}
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```
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## Introduction
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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.
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<p align="center">
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<img src="./figures/repst.png" height = "360" alt="" align=center />
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</p>
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- 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.
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<p align="center">
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<img src="./figures/method-detailed-illustration.png" height = "190" alt="" align=center />
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</p>
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## Requirements
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Use python 3.11 from MiniConda
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- torch==2.0.1
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- accelerate==0.28.0
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- einops==0.6.0
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- matplotlib==3.7.0
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- numpy==1.24.4
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- pandas==2.1.4
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- scikit_learn==1.3.2
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- scipy==1.11.4
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- tqdm==4.66.1
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- transformers==4.36.2
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To install all dependencies:
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```
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pip install -r requirements.txt
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```
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## Datasets
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# Pending
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You can access the well pre-processed datasets from [[Google Drive]](https://drive.google.com/), then place the downloaded contents under `./dataset`
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## Detailed usage
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Please refer to ```run.py``` for the detailed description of each hyperparameter.
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## Acknowledgement
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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.
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