What question did this study set out to answer?

Evaluate the zero-shot forecasting model TiRex against traditional forecasting techniques.

March 25, 2026Open Access

Forecasting Without Fine-Tuning: Zero-Shot Foundation Models vs. Traditional Approaches

Key Points

Evaluate the zero-shot forecasting model TiRex against traditional forecasting techniques.
Comparative evaluation of forecasting models using real-world production data.
Implementation of a rolling-origin validation framework for realistic forecasting conditions.
Measurement of forecast accuracy via Mean Absolute Error Percentage.
TiRex achieves comparable accuracy to traditional models without the need for fine-tuning.
Traditional models like XGBoost and Prophet show stable and reliable performance.
Findings indicate the potential for scalable deployment of TiRex in dynamic environments.

Abstract

This study conducts a comparative evaluation of the zero-shot forecasting model TiRex and five widely used forecasting techniques using real-world production data. The primary goal is to assess TiRex’s ability to forecast weekly SKU-level demand across multiple time series with varying lengths, data availability, and seasonal patterns. To simulate realistic forecasting conditions, a rolling-origin (walk-forward) validation framework is applied. Forecast accuracy is primarily measured using Mean Absolute Error Percentage, enabling consistent comparisons across different time series. Results show that while traditional models such as XGBoost and Prophet provide stable and reliable performance, the TiRex model achieves comparable accuracy without requiring task-specific fine-tuning. These findings highlight the potential of zero-shot forecasting for scalable, efficient deployment in dynamic and data-constrained production environments.

Forecasting Without Fine-Tuning: Zero-Shot Foundation Models vs. Traditional Approaches

Key Points

Abstract

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