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 for a precise ordering of food stock. From an environmental point of view, this ensures a low level of pre-consumer food waste, while from the managerial point of view, this is critical to 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-sale systems and allows for a straightforward human interpretation. Therefore, we propose two generalized additive models for predicting future sales. In an extensive evaluation, we consider two data sets consisting of multiple time series collected at a casual restaurant and a large staff canteen and covering a period of 20 months. 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.

准确的需求预测是成功管理餐厅和员工食堂的关键方面之一。特别是，正确预测菜单项的未来销售允许精确订购食物库存。从环境的角度来看，这确保了消费前食物浪费的水平较低，而从管理的角度来看，这对餐厅的盈利能力至关重要。因此，我们有兴趣预测给定菜单项的每日销售量的未来值。相应的时间序列显示出多个强季节性、趋势变化、数据差距和异常值。我们提出了一种预测方法，该方法完全基于从销售点系统检索的数据，并允许直接的人工解释。因此，我们提出了两种用于预测未来销售的广义可加模型。在广泛的评估中，我们考虑了两个数据集，这些数据集由在休闲餐厅和大型员工食堂收集的多个时间序列组成，涵盖了 20 个月的时间。我们表明，所提出的模型适合所考虑的餐厅数据的特征。此外，我们将我们的方法的预测性能与其他完善的预测方法的性能进行了比较。