标题： 全球预测模型是否需要频繁重新训练？ 显示英文标题

标题： Do global forecasting models require frequent retraining?

摘要： 在一个计算能力不断增强且环境意识日益增强的时代，组织面临着在预测模型的准确性与计算效率和可持续性之间取得平衡的关键挑战。全球预测模型通过降低计算时间获得了广泛关注。然而，使用新观测数据重新训练这些模型的常见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，分析了两种大型零售数据集中的各种重新训练场景，从连续更新到根本不重新训练。我们表明，较少频率的重新训练策略能够在保持预测准确性的同时减少计算成本，为大规模预测提供了一种更可持续的方法。我们还发现，当与较少频率的模型重新训练策略结合时，随着数据频率的增加，机器学习模型在减少预测成本方面略胜一筹。我们的研究结果挑战了频繁重新训练对于维持预测准确性这一传统观念。相反，在点预测和概率预测的情况下，周期性的重新训练提供了预测性能和效率之间的良好平衡。这些见解为寻求优化预测流程同时降低成本和能源消耗的组织提供了可操作的指导方针。 显示英文摘要

摘要： 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 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.