On the retraining frequency of global models in retail demand forecasting

TL;DR

This paper investigates how less frequent retraining of global retail demand forecasting models can maintain accuracy while reducing computational costs and energy consumption, challenging the need for continuous updates.

Contribution

It provides empirical evidence that periodic retraining balances forecast accuracy and efficiency, offering sustainable strategies for large-scale retail demand forecasting.

Findings

Less frequent retraining maintains accuracy

Reduced computational costs and energy use

ML models benefit from less frequent updates

Abstract

In an era of increasing computational capabilities and growing environmental consciousness, organizations face a critical challenge in balancing the accuracy of forecasting models with computational efficiency and sustainability. Global forecasting models, lowering the computational time, have gained significant attention over the years. However, the common practice of retraining these models with new observations raises important questions about the costs of forecasting. Using ten different machine learning and deep learning models, we analyzed various retraining scenarios, ranging from continuous updates to no retraining at all, across two large retail demand datasets. We showed that less frequent retraining strategies maintain the forecast accuracy while reducing the computational costs, providing a more sustainable approach to large-scale forecasting. We also found that machine learning models are a marginally better choice to reduce the costs of forecasting when coupled with less frequent model retraining strategies as the frequency of the data increases. Our findings challenge the conventional belief that frequent retraining is essential for maintaining forecasting accuracy. Instead, periodic retraining offers a good balance between predictive performance and efficiency, both in the case of point and probabilistic forecasting. These insights provide actionable guidelines for organizations seeking to optimize forecasting pipelines while reducing costs and energy consumption.