  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.
