<p>Global forecasting models used as base models.</p>

To assess it, we evaluated ten base models and eight ensemble configurations across two large-scale retail datasets, considering both point and probabilistic accuracy under varying retraining frequencies.

We showed that ensembles consistently improve forecasting performance, particularly in probabilistic settings. However, these gains come at a substantial computational cost, especially for larger, accuracy-driven ensembles. We found that reducing retraining frequency significantly lowers costs, with minimal impact on accuracy, particularly for point forecasts.