Update README.md

README.md

@@ -20,7 +20,8 @@ TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Serie
 **With less than 1 Million parameters, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** 
 
 
-TTM is accepted in NeurIPS 2024.
+TTM is accepted in NeurIPS 2024. 
+
 
 TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight 
 forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be 
@@ -196,24 +197,13 @@ work
 **BibTeX:**
 
 ```
-@misc{ekambaram2024tiny,
-      title={Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series}, 
-      author={Vijay Ekambaram and Arindam Jati and Pankaj Dayama and Sumanta Mukherjee and Nam H. Nguyen and Wesley M. Gifford and Chandra Reddy and Jayant Kalagnanam},
-      year={2024},
-      eprint={2401.03955},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG}
-}
-```
-
-**Bibtex:**
-
 @inproceedings{ekambaram2024tinytimemixersttms,
       title={Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series},
       author={Vijay Ekambaram and Arindam Jati and Pankaj Dayama and Sumanta Mukherjee and Nam H. Nguyen and Wesley M. Gifford and Chandra Reddy and Jayant Kalagnanam},
       booktitle={Advances in Neural Information Processing Systems (NeurIPS 2024)},
       year={2024},
 }
+```
 
 ## Model Card Authors