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@ -101,12 +101,12 @@ Please refer to ```run_main.py``` and ```run_m4.py``` for the detailed descripti
## Further Reading
[**Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook**](https://arxiv.org/abs/2310.10196)
1, [**Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook**](https://arxiv.org/abs/2310.10196), in *arXiv* 2023.
[\[GitHub Repo\]](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:
🌟 If you find this paper helpful, please consider to cite it in your research:
```
@article{jin2023lm4ts,
@ -117,5 +117,20 @@ Please refer to ```run_main.py``` and ```run_m4.py``` for the detailed descripti
}
```
2, [**Position Paper: What Can Large Language Models Tell Us about Time Series Analysis**](https://arxiv.org/abs/2402.02713), in *arXiv* 2024.
**Authors**: Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang*, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen*
🌟 If you find this paper helpful, please consider to cite it in your research:
```
@article{jin2024position,
title={Position Paper: What Can Large Language Models Tell Us about Time Series Analysis},
author={Ming Jin and Yifan Zhang and Wei Chen and Kexin Zhang and Yuxuan Liang and Bin Yang and Jindong Wang and Shirui Pan and Qingsong Wen},
journal={arXiv preprint arXiv:2402.02713},
year={2024}
}
```
## 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.