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@ -102,6 +102,7 @@ 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)
[\[GitHub Repo\]](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)
@ -117,4 +118,4 @@ Please refer to ```run_main.py``` and ```run_m4.py``` for the detailed descripti
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## 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.
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.