TiRex-2¶

Generalizing TiRex to Multivariate Data and Streaming

TiRex-2 is a pretrained time series foundation model that forecasts one or many target variates directly from their history, optionally conditioned on past and future-known covariates. A single checkpoint serves both univariate and multivariate forecasting, built on a recurrent architecture designed for efficient streaming settings — all zero-shot, with no task-specific training or fine-tuning.

TiRex-2 generalizes the original univariate model, TiRex, to multivariate forecasting with past and future covariates. See the Introduction for background and the paper for details.

Looking for TiRex-1?

This site documents TiRex-2. Documentation for the original univariate TiRex model lives at nx-ai.github.io/tirex — the two are separate projects and this site does not attempt to unify them.

Key facts¶

Zero-shot multivariate forecasting — TiRex-2 forecasts multiple target variates out of the box, without training or fine-tuning on your data.
Past and future-known covariates — TiRex-2 natively conditions on past covariates and future-known covariates, such as calendar features, holidays, promotions, or scheduled interventions.
Small active footprint — TiRex-2 activates 38.4M parameters in univariate mode and an additional 44.1M parameters for multivariate forecasting.

Where to go next¶

Installation — pip install tirex-2, Pixi setup, and gated Hugging Face weight access.
Quickstart — minimal sine-wave and covariate examples.
How-to guides — univariate/multivariate forecasting, covariates in depth, and what streaming does (and doesn't) mean in this open-source release.
Deployment — the Docker-based HTTP/MQTT/MCP inference server.
API reference — generated reference for the public tirex2 API.

TiRex-2 Pro¶

This repository is NXAI's open-source release. A Pro version extends TiRex-2 with:

Streaming: incremental forecast updates as new observations arrive, without recomputing over the full history.
Speed: performance-optimized inference, including optimization for dedicated hardware such as edge, embedded, and industrial PC deployments.
Finetuning: models fine-tuned on your data or with different pretraining.
Classification & Regression: TiRex-2 adapted for classification and regression tasks.

See TiRex-2 Pro for details, or contact contact@nx-ai.com.

Cite Our Work¶

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

@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},
}