Update README.md

README.md

@@ -11,7 +11,7 @@ library_name: tirex
 TiRex is a **time-series foundation model** designed for **time series forecasting**,
 with the emphasis to provide state-of-the-art forecasts for both short- and long-term forecasting horizon.
 TiRex is **35M parameter** small and is based on the **[xLSTM architecture](https://github.com/NX-AI/xlstm)** allowing fast and performant forecasts.
-The model is described in the paper [TiRex: Zero-Shot Forecasting across Long and Short Horizons with Enhanced In-Context Learning]() (TBA soon).
+The model is described in the paper [TiRex: Zero-Shot Forecasting across Long and Short Horizons with Enhanced In-Context Learning](https://arxiv.org/abs/2505.23719).
 
 ### Key Facts:
 
@@ -34,7 +34,7 @@ The inference code is available on [GitHub](https://github.com/NX-AI/tirex).
 TiRex is currently only tested on *Linux systems* and Nvidia GPUs with compute capability >= 8.0.
 If you want to use different systems, please check the [FAQ](#faq--troubleshooting).
 It's best to install TiRex in the specified conda environment.
-The respective conda dependency file is [requirements_py26.yaml](./requirements_py26.yaml).
+The respective conda dependency file is [requirements_py26.yaml](https://github.com/NX-AI/tirex/blob/main/requirements_py26.yaml).
 
 ```sh
 # 1) Setup and activate conda env from ./requirements_py26.yaml
@@ -68,7 +68,7 @@ data = torch.rand((5, 128))  # Sample Data (5 time series with length 128)
 forecast = model.forecast(context=data, prediction_length=64)
 ```
 
-We provide an extended quick start example in the [GitHub repository](https://github.com/NX-AI/tirex/examples/quick_start_tirex.ipynb).
+We provide an extended quick start example in the [GitHub repository](https://github.com/NX-AI/tirex/blob/main/examples/quick_start_tirex.ipynb).
 
 ### Troubleshooting / FAQ
 
@@ -87,7 +87,13 @@ and feel free to create a GitHub issue or start a discussion.
 If you use TiRex in your research, please cite our work: 
 
 ```bibtex
-TBA
+@article{auerTiRexZeroShotForecasting2025,
+  title = {{{TiRex}}: {{Zero-Shot Forecasting Across Long}} and {{Short Horizons}} with {{Enhanced In-Context Learning}}},
+  author = {Auer, Andreas and Podest, Patrick and Klotz, Daniel and B{\"o}ck, Sebastian and Klambauer, G{\"u}nter and Hochreiter, Sepp},
+  journal = {ArXiv},
+  volume = {2505.23719},   
+  year = {2025}
+}
 ```
 
 ## License