Add pipeline tag and link to paper (#3)

- Add pipeline tag and link to paper (1781f2ef300a2327af03dfc2dcce9a3e268c575d)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,6 +1,7 @@
 ---
-license: cc-by-4.0
 library_name: YingLong
+license: cc-by-4.0
+pipeline_tag: time-series-forecasting
 tags:
 - time-series
 - forecasting
@@ -10,11 +11,9 @@ tags:
 - large-time-series-models
 ---
 
-
 # YingLong
 
-YingLong model is introduced in this [paper](https://github.com/wxie9/YingLong/blob/main/YingLong_manuscript.pdf). This version is pre-trained on **78B** time points. More details can be found at our [github](https://github.com/wxie9/YingLong/).
-
+YingLong model is introduced in this [paper](https://huggingface.co/papers/2506.11029). This version is pre-trained on **78B** time points. More details can be found at our [github](https://github.com/wxie9/YingLong/).
 
  
 ## Quickstart
@@ -49,26 +48,10 @@ print(output.shape)
 
 A notebook example is also provided [here](https://github.com/wxie9/YingLong/blob/main/quickstart_zero_shot.ipynb). The sample codes for long-term forecasting tasks and gift-eval tasks are provided at [link](https://github.com/wxie9/YingLong/tree/main).
 
-
 <!-- ## Specification -->
 
-
 ## Citation
 Coming soon...
-<!-- ```
-@inproceedings{liutimer,
-  title={Timer: Generative Pre-trained Transformers Are Large Time Series Models},
-  author={Liu, Yong and Zhang, Haoran and Li, Chenyu and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng},
-  booktitle={Forty-first International Conference on Machine Learning}
-}
-
-@article{liu2024timer,
-  title={Timer-XL: Long-Context Transformers for Unified Time Series Forecasting},
-  author={Liu, Yong and Qin, Guo and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng},
-  journal={arXiv preprint arXiv:2410.04803},
-  year={2024}
-}
-``` -->
 
 ## Contact