tirex-2 0.1.0

TiRex-2 time series forecasting inference

TiRex-2: Generalizing TiRex to Multivariate Data and Streaming

This repository provides the pre-trained multivariate forecasting model TiRex-2 introduced in the paper TiRex-2: Generalizing TiRex to Multivariate Data and Streaming.

TiRex-2

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 and operates in a streaming fashion as new observations arrive — all zero-shot, with no task-specific training or fine-tuning.

Key facts

• Zero-shot multivariate forecasting: TiRex-2 forecasts multiple target variates out of the box, without training or fine-tuning on you 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.

Getting started

The environment is managed by Pixi. Run the following to install it on your machine

curl -fsSL https://pixi.sh/install.sh | sh
git clone https://github.com/NX-AI/tirex-2 && cd tirex-2
# activate the cpu-only env
eval "$(pixi shell-hook -e example)"  # to execute on GPUs use `example-cu128` or `example-cu126`

Minimal usage predicting a simple sine wave:

import matplotlib.pyplot as plt, torch
from tirex2 import TimeseriesType, load_model

model = load_model("NX-AI/TiRex-2", device="cpu")  # use `device="cuda"` if cuda is available
y = torch.sin(torch.arange(160).float() / 8)
ts = TimeseriesType(target=y[:128].unsqueeze(0), past_covariates=None, future_covariates=None)
forecast = model.forecast([ts], prediction_length=32, output_type="numpy")[0][0]

We provide predefined Pixi tasks showcasing examplary forecasts. These run in the CPU-only example environment by default:

• pixi run minimal runs above code and creates a plot of the forecast.
• pixi run comparison showcases the additional benefit of future known covariates in forecasting a target.

For a more interactive demo of TiRex-2, we also provide a quick-start notebook.

To reproduce our results for GIFT-Eval and fev-bench follow the instructions in ./examples/gifteval/ or ./examples/fevbench/, respectively.

Cite

If you use TiRex 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}, 
}