Update README.md

README.md

@@ -117,7 +117,20 @@ Please refer to ```run_main.py```, ```run_m4.py``` and ```run_pretrain.py``` for
 
 
 ## Further Reading
-1, [**Position Paper: What Can Large Language Models Tell Us about Time Series Analysis**](https://arxiv.org/abs/2402.02713), in *ICML* 2024.
+1, [**Foundation Models for Time Series Analysis: A Tutorial and Survey**](https://arxiv.org/pdf/2403.14735), in *KDD* 2024.
+
+**Authors**: Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen*
+
+```bibtex
+@inproceedings{liang2024foundation,
+  title={Foundation models for time series analysis: A tutorial and survey},
+  author={Liang, Yuxuan and Wen, Haomin and Nie, Yuqi and Jiang, Yushan and Jin, Ming and Song, Dongjin and Pan, Shirui and Wen, Qingsong},
+  booktitle={ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)},
+  year={2024}
+}
+```
+
+2, [**Position Paper: What Can Large Language Models Tell Us about Time Series Analysis**](https://arxiv.org/abs/2402.02713), in *ICML* 2024.
 
 **Authors**: Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang*, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen*
 
@@ -130,7 +143,7 @@ Please refer to ```run_main.py```, ```run_m4.py``` and ```run_pretrain.py``` for
 }
 ```
 
-2, [**Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook**](https://arxiv.org/abs/2310.10196), in *arXiv* 2023.
+3, [**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)
@@ -145,7 +158,7 @@ Please refer to ```run_main.py```, ```run_m4.py``` and ```run_pretrain.py``` for
 ```
 
 
-3, [**Transformers in Time Series: A Survey**](https://arxiv.org/abs/2202.07125), in IJCAI 2023.
+4, [**Transformers in Time Series: A Survey**](https://arxiv.org/abs/2202.07125), in IJCAI 2023.
 [\[GitHub Repo\]](https://github.com/qingsongedu/time-series-transformers-review)
 
 **Authors**: Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun
@@ -159,7 +172,7 @@ Please refer to ```run_main.py```, ```run_m4.py``` and ```run_pretrain.py``` for
 }
 ```
 
-4, [**TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting**](https://openreview.net/pdf?id=7oLshfEIC2), in ICLR 2024.
+5, [**TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting**](https://openreview.net/pdf?id=7oLshfEIC2), in ICLR 2024.
 [\[GitHub Repo\]](https://github.com/kwuking/TimeMixer)
 
 **Authors**: Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou