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.

翻译：在计算能力日益增强且环境意识不断提升的时代，组织在平衡预测模型的准确性、计算效率与可持续性方面面临关键挑战。全局预测模型因能降低计算时间，近年来受到广泛关注。然而，利用新观测数据对这些模型进行重训练的常规做法，引发了关于预测成本的重要问题。我们使用十种不同的机器学习和深度学习模型，在两个大型零售需求数据集上分析了从持续更新到完全不重训练的各种重训练场景。研究表明，较低频率的重训练策略能在保持预测精度的同时降低计算成本，为大规模预测提供了更可持续的途径。我们还发现，随着数据频率的增加，机器学习模型配合较低频率的模型重训练策略，能略微更有效地降低预测成本。这些发现挑战了"频繁重训练对维持预测精度至关重要"的传统观念。相反，无论是点预测还是概率预测，定期重训练都能在预测性能与效率之间实现良好平衡。这些见解为组织优化预测流程、同时降低成本和能耗提供了可操作的指导原则。