xLSTM-based time series model TiRex significantly outperforms competing models in forecasting accuracy
• Ranked #1 on official, international leaderboards
• 35M parameters - small and fast model
• Delivers expert-level forecasts accessible to non-experts
• Performs exceptionally well even with limited data
• Memory-efficient and customizable for embedded AI
Austrian based company NXAI unveils its first time series model, TiRex, based on the novel xLSTM architecture – and immediately claims the top spot in well-known international benchmark leaderboards. Despite having just 35 million parameters, TiRex is significantly smaller and more memory-efficient than its competitors. It not only excels in prediction accuracy but is also considerably faster.
“We’re no longer talking about marginal improvements – TiRex delivers a substantial leap in quality over other models, both for short- and long-term forecasts,” explains Prof. Dr. Sepp Hochreiter, Chief Scientist at NXAI in Linz.
NXAI’s TiRex model leverages in-context learning, enabling zero-shot forecasting – accurate predictions on new datasets without the need for additional training.
“This allows non-experts to use the model for forecasting and enables easy integration into existing workflows. Moreover, improvements in forecasting accuracy become particularly apparent when data availability is limited,” explains Andreas Auer, Researcher at NXAI.
This opens up new digital product models: for example, machinery manufacturers can offer customers TiRex-based solutions for optimization or commissioning. Thanks to in-context learning, the model adapts automatically to the customer's data – without retraining. “The key is how well a model generalizes to unseen time series – and TiRex excels at that,” adds Hochreiter.
A decisive advantage lies in the model’s ability to continuously monitor, analyse, and update the system state – known as state tracking. Transformer-based approaches lack this capability. TiRex, on the other hand, can approximate hidden or latent states over time, improving predictive performance. Its architecture offers another major benefit: it is adaptable to hardware and enables embedded AI applications.
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Model: https://huggingface.co/NX-AI/TiRex
Paper: https://arxiv.org/abs/2505.23719
Github: https://github.com/NX-AI
