大规模层次预测
Hierarchical Forecasting at Scale
AI总结：
本文提出一种稀疏层次损失函数，使单一底层模型能为数百万时间序列生成对任意截面或时间层次一致的预测，省去后处理步骤，并在M5数据集及欧洲电商平台bol上验证了显著的性能提升。
摘要：
现有的层次预测技术在时间序列数量增加时扩展性较差。我们提出使用单一底层预测模型，通过稀疏损失函数直接优化层次乘积和/或时间结构，为数百万条时间序列学习一致的预测结果。我们的稀疏层次损失函数的好处在于，它为实践者提供了一种方法，能够生成对任意选定的截面或时间层次都保持一致的底层预测。此外，与传统层次预测技术所需的后处理步骤相比，省去这一步骤降低了预测流程中预测阶段的计算成本。在公开的M5数据集上，与基线损失函数相比，我们的稀疏层次损失函数在RMSE指标上最多提升了10%。我们在bol（一家大型欧洲电商平台）的现有预测模型中实现了该稀疏层次损失函数，使产品级别的预测性能提升了2%。最后，我们发现在所定义的截面层次上评估预测性能时，预测性能提升了约5-10%。这些结果表明，我们的稀疏层次损失函数应用于大型电商平台的生产预测系统中具有实用价值。
英文摘要：
Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a single bottom-level forecast model by using a sparse loss function that directly optimizes the hierarchical product and/or temporal structure. The benefit of our sparse hierarchical loss function is that it provides practitioners a method of producing bottom-level forecasts that are coherent to any chosen cross-sectional or temporal hierarchy. In addition, removing the need for a post-processing step as required in traditional hierarchical forecasting techniques reduces the computational cost of the prediction phase in the forecasting pipeline. On the public M5 dataset, our sparse hierarchical loss function performs up to 10% (RMSE) better compared to the baseline loss function. We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform, resulting in an improved forecasting performance of 2% at the product level. Finally, we found an increase in forecasting performance of about 5-10% when evaluating the forecasting performance across the cross-sectional hierarchies that we defined. These results demonstrate the usefulness of our sparse hierarchical loss applied to a production forecasting system at a major e-commerce platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
