基于指数平滑和Holt线性趋势方法的电子商务中销售预测的分层鲁棒聚集
Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method
摘要：
我们重温经典的销售预测统计技术，如指数平滑和扩展（霍尔特的线性趋势法）的兴趣。我们这样做，考虑合奏预测，给出了这些经典的技术与不同的（组）参数调整的几个实例，并形成凸组合的合奏预测随着时间的推移，在一个强大的和顺序的方式。这背后的机器学习理论被称为“鲁棒在线聚合”，或“专家建议预测”，或“单个序列预测”（参见Cesa-Bianchi和Lugosi，2006）。我们将这种方法应用于电子商务公司Cdiscount提供的销售分层数据集，并在子子系列，子系列和销售项目系列的水平上进行输出预测，用于各种预测范围（最多6周）。所实现的性能优于通过在训练集上优化调整经典技术并在测试集上使用它们的预测所获得的性能。从内在的角度来看，性能也很好（就平均绝对误差百分比而言）。虽然在子子家族、子家族和家族的层面上获得这些更好的销售预测本身就很有趣，但我们也建议在预测项目层面的需求时将它们用作附加功能。
英文摘要：
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.
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