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README.md

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-#  <img src="https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/ytpsIAr98keUvNouoOVmb.png" alt="Image description" style="width:65px;height:65px;"> Aurora: Towards Universal Generative Multimodal Time Series Forecasting
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+<img alt="intro" src="https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/ytpsIAr98keUvNouoOVmb.png" width="30%"/>
+  <h1> Aurora: Towards Universal Generative Multimodal Time Series Forecasting </h1>
+  The official code repo of our ICLR 26's paper: <a href="https://arxiv.org/pdf/2509.22295">Aurora: Towards Universal Generative Multimodal Time Series Forecasting</a>
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+[![ICLR](https://img.shields.io/badge/ICLR'26-Aurora-orange)](https://arxiv.org/pdf/2509.22295)  [![Python](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)  [![PyTorch](https://img.shields.io/badge/PyTorch-2.4.1-blue)](https://pytorch.org/)  ![Stars](https://img.shields.io/github/stars/decisionintelligence/Aurora) 
+</div>
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-**The official code repo of our ICLR 26's paper: [Aurora: Towards Universal Generative Multimodal Time Series Forecasting](https://arxiv.org/pdf/2509.22295)**
 
-[![ICLR](https://img.shields.io/badge/ICLR'26-Aurora-orange)](https://arxiv.org/pdf/2509.22295)  [![Python](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)  [![PyTorch](https://img.shields.io/badge/PyTorch-2.4.1-blue)](https://pytorch.org/)  ![Stars](https://img.shields.io/github/stars/decisionintelligence/Aurora) 
 
 ## Introduction