Time Series Playbook
Dive into theory and hands-on guides for forecasting with TiRex.
Explore forecasting →Benchmarks
Datasets, metrics (sMAPE/MASE/CRPS), and reproducible evaluation.
View results →Deploy
Packaging tips, ONNX/TensorRT export (coming), and edge deployment.
Deployment →Cite Our Work
If you use TiRex or xLSTM in your research, please cite our papers:
@inproceedings{auer:25tirex,
title = {{{TiRex}}: {{Zero-Shot Forecasting Across Long}} and {{Short Horizons}} with {{Enhanced In-Context Learning}}},
author = {Andreas Auer and Patrick Podest and Daniel Klotz and Sebastian B{\"o}ck and G{\"u}nter Klambauer and Sepp Hochreiter},
booktitle = {The Thirty-Ninth Annual Conference on Neural Information Processing Systems},
year = {2025}
url = {https://arxiv.org/abs/2505.23719},
}
@inproceedings{auer:25tirexclassification,
title = {Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification},
author = {Andreas Auer and Daniel Klotz and Sebastinan B{\"o}ck and Sepp Hochreiter},
booktitle = {NeurIPS 2025 Workshop on Recent Advances in Time Series Foundation Models (BERT2S)},
year = {2025},
url = {https://arxiv.org/abs/2510.26777},
}
@inproceedings{beck:24xlstm,
title = {xLSTM: Extended Long Short-Term Memory},
author = {Maximilian Beck and Korbinian Pöppel and Markus Spanring and Andreas Auer and Oleksandra Prudnikova and Michael Kopp and Günter Klambauer and Johannes Brandstetter and Sepp Hochreiter},
booktitle = {Thirty-eighth Conference on Neural Information Processing Systems},
year = {2024},
url = {https://arxiv.org/abs/2405.04517},