论文标题
基于指数平滑和霍尔特的线性趋势方法，电子商务中销售预测的良好稳定汇总
Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method
论文作者
论文摘要
我们重新审视了经典统计技术对销售预测的兴趣，例如指数平滑和扩展（作为Holt的线性趋势方法）。我们这样做是通过考虑集合预测的，这些预测是由这些经典技术的几种实例给出的，并以不同的（一组）参数调谐，并以稳健和顺序的方式形成一体元素预测元素的凸组合。此背后的机器学习理论称为“强大的在线聚合”或“使用专家建议的预测”或“对单个序列的预测”（参见Cesa-Bianchi和Lugosi，2006年）。我们将此方法应用于电子商务公司CDISCOUNT提供的层次数据集，并在售出的各种预测范围（最高6周）中提供的subsubfamilies，subfriase and subfories subfamilies，subfriase and subfories subfamilies and themains of Uppast销售数据集。实现的性能要比通过在火车组上最佳调整经典技术并在测试集上使用其预测来获得的要好。从固有的角度来看（就误差的平均绝对百分比而言）也是好的。尽管可以在子家庭，子家庭和家庭的水平上获得这些更好的销售预测，但本身很有趣，但我们也建议将它们用作项目水平的预测需求时将其用作附加功能。
We revisit the interest of classical statistical techniques for sales forecasting like exponential smoothing and extensions thereof (as Holt's linear trend method). We do so by considering ensemble forecasts, given by several instances of these classical techniques tuned with different (sets of) parameters, and by forming convex combinations of the elements of ensemble forecasts over time, in a robust and sequential manner. The machine-learning theory behind this is called "robust online aggregation", or "prediction with expert advice", or "prediction of individual sequences" (see Cesa-Bianchi and Lugosi, 2006). We apply this methodology to a hierarchical data set of sales provided by the e-commerce company Cdiscount and output forecasts at the levels of subsubfamilies, subfamilies and families of items sold, for various forecasting horizons (up to 6-week-ahead). The performance achieved is better than what would be obtained by optimally tuning the classical techniques on a train set and using their forecasts on the test set. The performance is also good from an intrinsic point of view (in terms of mean absolute percentage of error). While getting these better forecasts of sales at the levels of subsubfamilies, subfamilies and families is interesting per se, we also suggest to use them as additional features when forecasting demand at the item level.
