The recent M5 competition has advanced the state-of-the-art in retail
forecasting. However, we notice important differences between the competition
challenge and the challenges we face in a large e-commerce company. The
datasets in our scenario are larger (hundreds of thousands of time series), and
e-commerce can afford to have a larger assortment than brick-and-mortar
retailers, leading to more intermittent data. To scale to larger dataset sizes
with feasible computational effort, firstly, we investigate a two-layer
hierarchy and propose a top-down approach to forecasting at an aggregated level
with less amount of series and intermittency, and then disaggregating to obtain
the decision-level forecasts. Probabilistic forecasts are generated under
distributional assumptions. Secondly, direct training at the lower level with
subsamples can also be an alternative way of scaling. Performance of modelling
with subsets is evaluated with the main dataset. Apart from a proprietary
dataset, the proposed scalable methods are evaluated using the Favorita dataset
and the M5 dataset. We are able to show the differences in characteristics of
the e-commerce and brick-and-mortar retail datasets. Notably, our top-down
forecasting framework enters the top 50 of the original M5 competition, even
with models trained at a higher level under a much simpler setting.

对于规模更大的电子商务公司的预测挑战，在较少的系列和间歇性数据的聚合级别上进行自顶向下的预测，并进行分解以获取决策级别的预测，通过分布假设产生概率预测，并使用子样本进行直接训练的可扩展方法。