Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
---
>
> 🙋 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{jin2023time,
title={Time-llm: Time series forecasting by reprogramming large language models},
author={Jin, Ming and Wang, Shiyu and Ma, Lintao and Chu, Zhixuan and Zhang, James Y and Shi, Xiaoming and Chen, Pin-Yu and Liang, Yuxuan and Li, Yuan-Fang and Pan, Shirui and others},
booktitle={International Conference on Learning Representations},
year={2024}
}
```
## Introdcution
Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact.
Notably, we show that time series analysis (e.g., forecasting) can be cast as yet another "language task" that can be effectively tackled by an off-the-shelf LLM.
- Time-LLM comprises two key components: (1) reprogramming the input time series into text prototype representations that are more natural for the LLM, and (2) augmenting the input context with declarative prompts (e.g., domain expert knowledge and task instructions) to guide LLM reasoning.
## Requirements
- accelerate==0.20.3
- einops==0.7.0
- matplotlib==3.7.0
- numpy==1.23.5
- pandas==1.5.3
- scikit_learn==1.2.2
- scipy==1.5.4
- torch==2.0.1
- tqdm==4.65.0
- peft==0.4.0
- transformers==4.31.0
- deepspeed==0.13.0
To install all dependencies:
```
pip install -r requirements.txt
```
## Datasets
You can access the well pre-processed datasets from [[Google Drive]](https://drive.google.com/file/d/1NF7VEefXCmXuWNbnNe858WvQAkJ_7wuP/view?usp=sharing), then place the downloaded contents under `./dataset`
## Quick Demos
1. Download datasets and place them under `./dataset`
2. Tune the model. We provide five experiment scripts for demonstration purpose under the folder `./scripts`. For example, you can evaluate on ETT datasets by:
```bash
bash ./scripts/TimeLLM_ETTh1.sh
```
```bash
bash ./scripts/TimeLLM_ETTh2.sh
```
```bash
bash ./scripts/TimeLLM_ETTm1.sh
```
```bash
bash ./scripts/TimeLLM_ETTm2.sh
```
## Detailed usage
Please refer to ```run_main.py``` and ```run_m4.py``` for the detailed description of each hyperparameter.
## Further Reading
[**Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook**](https://arxiv.org/abs/2310.10196)
[\[GitHub Repo\]](https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM)
**Authors**: Ming Jin, Qingsong Wen*, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li (IEEE Fellow), Shirui Pan*, Vincent S. Tseng (IEEE Fellow), Yu Zheng (IEEE Fellow), Lei Chen (IEEE Fellow), Hui Xiong (IEEE Fellow)
🌟 If you find this resource helpful, please consider to cite it in your research:
```
@article{jin2023lm4ts,
title={Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook},
author={Ming Jin and Qingsong Wen and Yuxuan Liang and Chaoli Zhang and Siqiao Xue and Xue Wang and James Zhang and Yi Wang and Haifeng Chen and Xiaoli Li and Shirui Pan and Vincent S. Tseng and Yu Zheng and Lei Chen and Hui Xiong},
journal={arXiv preprint arXiv:2310.10196},
year={2023}
}
```
## Acknowledgement
Our implementation adapts [Time-Series-Library](https://github.com/thuml/Time-Series-Library) and [GPT4TS](https://github.com/DAMO-DI-ML/NeurIPS2023-One-Fits-All) as the code base and have extensively modified it to our purposes. We thank the authors for sharing their implementations and related resources.