Revamp README: Add News, Coverage, Logo, Shields, Emojis, Zero-Shot results (#56)

*Description of changes:* This PR revamps the README. 


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---------

Co-authored-by: Abdul Fatir Ansari <[email protected]>

Author: abdulfatir

README.md

@@ -1,5 +1,21 @@
+<div align="center">
+<img src="figures/chronos-logo.png" width="60%">
+
 # Chronos: Learning the Language of Time Series
 
+[![preprint](https://img.shields.io/static/v1?label=arXiv&message=2403.07815&color=B31B1B&logo=arXiv)](https://arxiv.org/abs/2403.07815)
+[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444)
+[![License: MIT](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+</div>
+
+## 🚀 News
+
+- **08 Apr 2024**: 🧪 Experimental [MLX inference support](https://github.com/amazon-science/chronos-forecasting/tree/mlx) added. If you have an Apple Silicon Mac, you can now obtain significantly faster forecasts from Chronos compared to CPU inference. This provides an alternative way to exploit the GPU on your Apple Silicon Macs together with the "mps" support in PyTorch.
+- **25 Mar 2024**: [v1.1.0 released](https://github.com/amazon-science/chronos-forecasting/releases/tag/v1.1.0) with inference optimizations and `pipeline.embed` to extract encoder embeddings from Chronos.
+- **13 Mar 2024**: Chronos [paper](https://arxiv.org/abs/2403.07815) and inference code released.
+
+## ✨ Introduction
+
 Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
 
 For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815).
@@ -12,12 +28,12 @@ For details on Chronos models, training data and procedures, and experimental re
   </span>
 </p>
 
----
-
-## Architecture
+### Architecture
 
 The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters.
 
+<div align="center">
+
 | Model                                                                  | Parameters | Based on                                                               |
 | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- |
 | [**chronos-t5-tiny**](https://huggingface.co/amazon/chronos-t5-tiny)   | 8M         | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny)   |
@@ -26,7 +42,21 @@ The models in this repository are based on the [T5 architecture](https://arxiv.o
 | [**chronos-t5-base**](https://huggingface.co/amazon/chronos-t5-base)   | 200M       | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base)   |
 | [**chronos-t5-large**](https://huggingface.co/amazon/chronos-t5-large) | 710M       | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) |
 
-## Usage
+</div>
+
+### Zero-Shot Results
+
+The following figure showcases the remarkable **zero-shot** performance of Chronos models on 27 datasets against local models, task-specific models and other pretrained models. For details on the evaluation setup and other results, please refer to [the paper](https://arxiv.org/abs/2403.07815). 
+
+<p align="center">
+  <img src="figures/zero_shot-agg_scaled_score.png" width="80%">
+  <br />
+  <span>
+    Fig. 2: Performance of different models on Benchmark II, comprising 27 datasets <b>not seen</b> by Chronos models during training. This benchmark provides insights into the zero-shot performance of Chronos models against local statistical models, which fit parameters individually for each time series, task-specific models <i>trained on each task</i>, and pretrained models trained on a large corpus of time series. Pretrained Models (Other) indicates that some (or all) of the datasets in Benchmark II may have been in the training corpus of these models. The probabilistic (WQL) and point (MASE) forecasting metrics were normalized using the scores of the Seasonal Naive baseline and aggregated through a geometric mean to obtain the Agg. Relative WQL and MASE, respectively.
+  </span>
+</p>
+
+## 📈 Usage
 
 To perform inference with Chronos models, install this package by running:
 
@@ -106,7 +136,27 @@ embeddings, tokenizer_state = pipeline.embed(context)
 ```
 
 
-## Citation
+## 🔥 Coverage
+
+- [Adapting language model architectures for time series forecasting](https://www.amazon.science/blog/adapting-language-model-architectures-for-time-series-forecasting) (Amazon Science blog post)
+- [Amazon AI Researchers Introduce Chronos: A New Machine Learning Framework for Pretrained Probabilistic Time Series Models](https://www.marktechpost.com/2024/03/15/amazon-ai-researchers-introduce-chronos-a-new-machine-learning-framework-for-pretrained-probabilistic-time-series-models/) (Marktechpost blog post)
+- [Chronos: The Rise of Foundation Models for Time Series Forecasting](https://towardsdatascience.com/chronos-the-rise-of-foundation-models-for-time-series-forecasting-aaeba62d9da3) (Towards Data Science blog post by Luís Roque and Rafael Guedes)
+- [Moirai: Time Series Foundation Models for Universal Forecasting](https://towardsdatascience.com/moirai-time-series-foundation-models-for-universal-forecasting-dc93f74b330f) (Towards Data Science blog post by Luís Roque and Rafael Guedes, includes comparison of Chronos with Moirai)
+- [Chronos: The Latest Time Series Forecasting Foundation Model by Amazon](https://towardsdatascience.com/chronos-the-latest-time-series-forecasting-foundation-model-by-amazon-2687d641705a) (Towards Data Science blog post by Marco Peixeiro)
+  - The original article had a critical bug affecting the metric computation for Chronos. We opened a [pull request](https://github.com/marcopeix/time-series-analysis/pull/10) to fix it.
+- [How to Effectively Forecast Time Series with Amazon's New Time Series Forecasting Model](https://towardsdatascience.com/how-to-effectively-forecast-time-series-with-amazons-new-time-series-forecasting-model-9e04d4ccf67e) (Towards Data Science blog post by Eivind Kjosbakken)
+- [Chronos: Learning the Language of Time Series](https://minimizeregret.com/linked/2024/03/27/chronos-forecasting/) (Minimize Regret blog post by Tim Radtke)
+- [Chronos: Another Zero-Shot Time Series Forecaster LLM](https://levelup.gitconnected.com/chronos-another-zero-shot-time-series-forecaster-llm-0e80753a7ad0) (Level Up Coding blog post by Level Up Coding AI TutorMaster)
+- [Paper Review: Chronos: Learning the Language of Time Series](https://andlukyane.com/blog/paper-review-chronos) (Review by Andrey Lukyanenko)
+- [Foundation Models for Forecasting: the Future or Folly?](https://insights.radix.ai/blog/foundation-models-for-forecasting-the-future-or-folly) (Blog post by Radix)
+- [Learning the Language of Time Series with Chronos](https://medium.com/@ManueleCaddeo/learning-the-language-of-time-series-with-chronos-fea7d0fedde4) (Medium post by Manuele Caddeo)
+- [The latest advancement in Time Series Forecasting from AWS: Chronos](https://medium.com/chat-gpt-now-writes-all-my-articles/the-latest-advancement-in-time-series-forecasting-from-aws-chronos-python-code-included-0205d01248f3) (Medium post by Abish Pius)
+- [Decoding the Future: How Chronos Redefines Time Series Forecasting with the Art of Language](https://medium.com/@zamalbabar/decoding-the-future-how-chronos-redefines-time-series-forecasting-with-the-art-of-language-cecc2174e400) (Medium post by Zamal)
+- [Comparison of Chronos against the SCUM ensemble of statistical models](https://github.com/Nixtla/nixtla/tree/main/experiments/amazon-chronos) (Benchmark by Nixtla)
+  - We opened a [pull request](https://github.com/Nixtla/nixtla/pull/281) extending the analysis to 28 datasets (200K+ time series) and showing that **zero-shot** Chronos models perform comparably to this strong ensemble of 4 statistical models while being significantly faster on average. Our complete response can be [found here](https://www.linkedin.com/pulse/extended-comparison-chronos-against-statistical-ensemble-ansari-4aste/).
+- [Comparison of Chronos against a variety of forecasting models](https://www.linkedin.com/feed/update/urn:li:activity:7178398371815051267/) (Benchmark by ReadyTensor)
+
+## 📝 Citation
 
 If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815):
 
@@ -119,10 +169,10 @@ If you find Chronos models useful for your research, please consider citing the
 }
 ```
 
-## Security
+## 🛡️ Security
 
 See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
 
-## License
+## 📃 License
 
 This project is licensed under the Apache-2.0 License.