  Accurate demand forecasting is one of the key aspects for successfully
managing restaurants and staff canteens. In particular, properly predicting
future sales of menu items allows a precise ordering of food stock. From an
environmental point of view, this ensures maintaining a low level of
pre-consumer food waste, while from the managerial point of view, this is
critical to guarantee the profitability of the restaurant. Hence, we are
interested in predicting future values of the daily sold quantities of given
menu items. The corresponding time series show multiple strong seasonalities,
trend changes, data gaps, and outliers. We propose a forecasting approach that
is solely based on the data retrieved from Point of Sales systems and allows
for a straightforward human interpretation. Therefore, we propose two
generalized additive models for predicting the future sales. In an extensive
evaluation, we consider two data sets collected at a casual restaurant and a
large staff canteen consisting of multiple time series, that cover a period of
20 months, respectively. We show that the proposed models fit the features of
the considered restaurant data. Moreover, we compare the predictive performance
of our method against the performance of other well-established forecasting
approaches.
