员工餐厅餐饮销售额的贝叶斯预测方法
A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants
摘要：
准确的需求预测是成功管理餐厅和员工食堂的关键因素之一。特别是，正确预测菜单项的未来销售可以精确订购食品库存。从环境的角度来看，这确保了消费前的食物浪费保持在低水平，而从管理的角度来看，这对保证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、数据缺口和异常值。我们提出了一种预测方法，它完全基于从销售点系统检索的数据，并允许一个简单的人类解释。因此，我们提出了两个预测未来销售额的广义可加模型。在一个广泛的评估，我们认为两个数据集收集在一个休闲餐厅和一个大型员工食堂组成的多个时间序列，涵盖了20个月的时间，分别。我们表明，所提出的模型适合所考虑的餐厅数据的功能。此外，我们比较我们的方法对其他成熟的预测方法的性能的预测性能。
英文摘要：
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.
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