标题： 全球预测模型的再训练频率 显示英文标题

标题： On the retraining frequency of global forecasting models

摘要： 在计算能力不断增强和环保意识日益增强的时代，组织面临着一个关键挑战，即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。 全球预测模型，降低计算时间，多年来引起了广泛关注。 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。 我们使用了十种不同的机器学习和深度学习模型，分析了各种重新训练场景，从持续更新到完全不重新训练，涵盖了两个大型零售数据集。 我们表明，较少频率的重新训练策略可以在保持预测准确性的同时减少计算成本，为大规模预测提供更可持续的方法。 我们还发现，当数据频率增加时，将机器学习模型与较少频率的模型重新训练策略结合，是减少预测成本的一个稍微更好的选择。 我们的研究结果挑战了传统的观点，即频繁重新训练对于保持预测准确性是必不可少的。 相反，定期重新训练在点预测和概率预测的情况下，能够在预测性能和效率之间提供良好的平衡。 这些见解为寻求优化预测流程同时降低成本和能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.