Introduction

TiRex-2 is introduced in the paper TiRex-2: Generalizing TiRex to Multivariate Data and Streaming (arXiv:2607.01204).

From TiRex to TiRex-2

The original TiRex model is a univariate, zero-shot time series forecasting model built on the xLSTM architecture. TiRex-2 generalizes it along two axes:

• Multivariate forecasting: a single checkpoint forecasts one or many target variates jointly, and can condition on past covariates and future-known covariates (e.g. calendar features, holidays, promotions, or scheduled interventions) alongside the target history.
• Streaming-oriented architecture: TiRex-2 is built on a recurrent architecture (extending the xLSTM-based design) chosen for efficient streaming settings.

Both univariate and multivariate forecasting are served zero-shot, without any task-specific training or fine-tuning, from the same pretrained checkpoint published on Hugging Face.

What "streaming-oriented" means in this release

The recurrent architecture is what makes efficient incremental inference possible in principle, but this open-source release does not itself expose an incremental, state-carrying forecast API — every call to forecast recomputes over the full context array you pass in. Incremental (no-recompute) streaming updates are part of TiRex-2 Pro. See How-to: Streaming for the full explanation.

Citation

If you use TiRex-2 in your research, please cite:

@misc{podest2026tirex2generalizingtirexmultivariate,
      title={TiRex-2: Generalizing TiRex to Multivariate Data and Streaming},
      author={Patrick Podest and Marco Pichler and Elias Bürger and Levente Zólyomi and Bernhard Voggenberger and Wilhelm Berghammer and Daniel Klotz and Sebastian Böck and Günter Klambauer and Sepp Hochreiter},
      year={2026},
      eprint={2607.01204},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2607.01204},
}