TiRex EU AI Act Compliance Profile

TiRex

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.

Key Facts:

• 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.

• Quantile Predictions: TiRex provides both point estimates and quantile estimates.

• State-of-the-art Performance over Long and Short Horizons: TiRex achieves top scores in various time series forecasting benchmarks, see GiftEval and ChronosZS. These benchmark show that TiRex provides great performance for both long and short-term forecasting.

Documentation

A detailed documentation of TiRex can be found here: https://nx-ai.github.io/tirex/

Installation

pip install tirex-ts

Install with additional input/output adapter:

pip install "tirex-ts[gluonts,hfdataset]"

You can also install TiRex with all extras at once using:

pip install "tirex-ts[all]"

TiRex is currently only tested on Linux and MacOS.

Quick Start

import torch
from tirex import load_model, ForecastModel

model: ForecastModel = load_model("NX-AI/TiRex")
data = torch.rand((5, 128))  # Sample Data (5 time series with length 128)
quantiles, mean = model.forecast(context=data, prediction_length=64)

We provide an extended quick start example in examples/quick_start_tirex.ipynb. This notebook also shows how to use the different input and output types of you time series data. You can also run it in [Google Colab](https://colab.research.google.com/github/NX-AI/tirex/

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