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TiRex

NX-AI

This repository provides the pre-trained forecasting model TiRex introduced in the paper TiRex: Zero-Shot Forecasting across Long and Short Horizons with Enhanced In-Context Learning. TiRex is a 35M parameter pre-trained time series forecasting model based on xLSTM. - **Zero-Shot Forecasting**: TiRex is a pre-trained model that performs time series forecasting without requiring any training on your data. Simply download and use it.

Tabular & time series
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TiRex-2

NX-AI

This repository provides the pretrained TiRex-2 model and inference code for zero-shot multivariate forecasting with past and future-known covariates, as introduced in 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 and operates in a streaming fashion as new observations arrive — all zero-shot, with no task-specific training or fine-tuning. TiRex-2 generalizes our original univariate model, TiRex, to multivariate forecasting with past and future covariates.

Tabular & time series
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This xLSTM-7B was pre-trained on the DCLM and selected high-quality data for in a total of approx. 2.3 T tokens using the `xlstm-jax` framework. First, install `xlstm`, which now uses the `mlstm_kernels` package for triton kernels (tested on python 3.11): If you cannot or do not want to use the triton kernels, you can change them to native PyTorch implementations:

Category pending
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