Initial model commit

README.md

@@ -0,0 +1,113 @@
+---
+datasets:
+- autogluon/chronos_datasets
+- Salesforce/GiftEvalPretrain
+pipeline_tag: time-series-forecasting
+library_name: tirex
+license: other
+license_link: https://huggingface.co/NX-AI/TiRex/blob/main/LICENSE
+license_name: nx-ai-community-license
+---
+
+# Changes from 1.0 to 1.1
+
+The 1.1 release introduces **long period normalisation**, a method applied solely during inference.
+
+## Version 1.1-gifteval
+
+This specific version (1.1-gifteval) includes all 1.1 improvements plus the pretraining dataset has been cleaned
+to remove overlaps with the GIFT-Eval test dataset.
+
+# 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](https://arxiv.org/abs/2505.23719).
+
+### Key Facts:
+
+- **Zero-Shot Forecasting**:
+  TiRex performs forecasting without any training on your data. Just download and forecast.
+
+- **Quantile Predictions**:
+  TiRex not only provides point estimates but provides quantile estimates.
+
+- **State-of-the-art Performance over Long and Short Horizons**:
+  TiRex achieves top scores in various time series forecasting benchmarks, see [GiftEval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [ChronosZS](https://huggingface.co/spaces/autogluon/fev-leaderboard).
+  These benchmark show that TiRex provides great performance for both long and short-term forecasting.
+
+## Quick Start
+
+The inference code is available on [GitHub](https://github.com/NX-AI/tirex).
+
+### Installation
+
+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 in the code repository](https://github.com/NX-AI/tirex?tab=readme-ov-file#faq--troubleshooting).
+It's best to install TiRex in the specified conda environment.
+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
+git clone github.com/NX-AI/tirex
+conda env create --file ./tirex/requirements_py26.yaml
+conda activate tirex
+
+# 2) [Mandatory] Install Tirex
+
+## 2a) Install from source
+git clone github.com/NX-AI/tirex  # if not already cloned before
+cd tirex
+pip install -e .
+
+# 2b) Install from PyPi (will be available soon)
+
+# 2) Optional: Install also optional dependencies
+pip install .[gluonts]      # enable gluonTS in/output API
+pip install .[hfdataset]    # enable HuggingFace datasets in/output API
+pip install .[notebooks]    # To run the example notebooks
+```
+
+### Inference Example
+
+```python
+import torch
+from tirex import load_model, ForecastModel
+
+model: ForecastModel = load_model("NX-AI/TiRex-1.1-gifteval")
+data = torch.rand((5, 128))  # Sample Data (5 time series with length 128)
+forecast = model.forecast(context=data, prediction_length=64, resample_strategy="frequency")
+```
+
+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
+
+If you have problems please check the FAQ / Troubleshooting section in the [GitHub repository](https://github.com/NX-AI/tirex)
+and feel free to create a GitHub issue or start a discussion.
+
+
+### Training Data
+
+- [chronos_datasets](https://huggingface.co/datasets/autogluon/chronos_datasets) (Subset - Zero Shot Benchmark data is not used for training - details in the paper)
+- [GiftEvalPretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain) (Subset - details in the paper)
+- Synthetic Data
+
+## Cite
+
+If you use TiRex in your research, please cite our work: 
+
+```bibtex
+@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
+
+TiRex is licensed under the [NXAI community license](./LICENSE).

model.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9a62f5669e64e9d6e7a4395e109db11e374faf31f8cfafdd1d93dbf6880e9bb
+size 141230262