Update README

README.md

@@ -10,29 +10,40 @@ tags:
   - time-series
 ---
 
-# Chronos-Bolt-Mini
+# Chronos-Bolt⚡ (Mini)
 
-Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. Chronos-Bolt models are based on the [T5 architecture](https://arxiv.org/abs/1910.10683) and are available in the following sizes.
+Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the [T5 encoder-decoder architecture](https://arxiv.org/abs/1910.10683) and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the [original Chronos](https://arxiv.org/abs/2403.07815) models of the same size.
+
+The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.
+
+<center>
+<img src="https://autogluon.s3.amazonaws.com/images/chronos_bolt_speed.svg" width="50%"/>
+</center>
+
+Chronos-Bolt models are not only significantly faster but also more accurate than the original Chronos models. The following plot reports the probabilistic and point forecasting performance of Chronos-Bolt in terms of the [Weighted Quantile Loss (WQL)](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.WQL) and the [Mean Absolute Scaled Error (MASE)](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.MASE), respectively, aggregated over 27 datasets (see the [Chronos paper](https://arxiv.org/abs/2403.07815) for details on this benchmark). Remarkably, despite having no prior exposure to these datasets during training, the zero-shot Chronos-Bolt models outperform commonly used statistical models and deep learning models that have been trained on these datasets (highlighted by *). Furthermore, they also perform better than other FMs, denoted by a +, which indicates that these models were pretrained on certain datasets in our benchmark and are not entirely zero-shot. Notably, Chronos-Bolt (Base) also surpasses the original Chronos (Large) model in terms of the forecasting accuracy while being over 600 times faster.
+
+<center>
+<img src="https://autogluon.s3.amazonaws.com/images/chronos_bolt_accuracy.svg" width="80%"/>
+</center>
+
+Chronos-Bolt models are available in the following sizes.
 
 
 <div align="center">
 
 | Model                                                                         | Parameters | Based on                                                               |
 | ----------------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- |
-| [**chronos-bolt-tiny**](https://huggingface.co/autogluon/chronos-bolt-tiny)   | 9M         | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny)   |
-| [**chronos-bolt-mini**](https://huggingface.co/autogluon/chronos-bolt-mini)   | 21M        | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini)   |
-| [**chronos-bolt-small**](https://huggingface.co/autogluon/chronos-bolt-small) | 48M        | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) |
-| [**chronos-bolt-base**](https://huggingface.co/autogluon/chronos-bolt-base)   | 205M       | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base)   |
+| [**chronos-bolt-tiny**](https://huggingface.co/amazon/chronos-bolt-tiny)   | 9M         | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny)   |
+| [**chronos-bolt-mini**](https://huggingface.co/amazon/chronos-bolt-mini)   | 21M        | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini)   |
+| [**chronos-bolt-small**](https://huggingface.co/amazon/chronos-bolt-small) | 48M        | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) |
+| [**chronos-bolt-base**](https://huggingface.co/amazon/chronos-bolt-base)   | 205M       | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base)   |
 
 </div>
 
 
 ## Usage with AutoGluon
 
-> [!WARNING]  
-> Chronos-Bolt models will be available in the next stable release of AutoGluon, so the following instructions will only work once AutoGluon 1.2 has been released.
-
-The recommended way of using Chronos for production use cases is through [AutoGluon](https://auto.gluon.ai/stable/index.html), which features effortless fine-tuning, ensembling with other statistical and machine learning models for time series forecasting as well as seamless deployments on AWS with SageMaker.
+The recommended way of using Chronos for production use cases is through [AutoGluon](https://auto.gluon.ai/stable/index.html), which features effortless fine-tuning, augmenting Chronos models with exogenous information through covariate regressors, ensembling with other statistical and machine learning models, as well as seamless deployments on AWS with SageMaker.
 Check out the AutoGluon Chronos [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html).
 
 A minimal example showing how to perform zero-shot inference using Chronos-Bolt with AutoGluon:
@@ -98,10 +109,12 @@ If you find Chronos or Chronos-Bolt models useful for your research, please cons
 
 ```
 @article{ansari2024chronos,
-  author  = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},
-  title   = {Chronos: Learning the Language of Time Series},
-  journal = {arXiv preprint arXiv:2403.07815},
-  year    = {2024}
+    title={Chronos: Learning the Language of Time Series},
+    author={Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},
+    journal={Transactions on Machine Learning Research},
+    issn={2835-8856},
+    year={2024},
+    url={https://openreview.net/forum?id=gerNCVqqtR}
 }
 ```