Food sales prediction is concerned with estimating future sales of companiesin the food industry, such as supermarkets, groceries, restaurants, bakeries and patisseries.Accurate short-term sales prediction allows companies to minimize stocked and expiredproducts inside stores and at the same time avoid missing sales. This paper reviews existingmachine learning approaches for food sales prediction. It discusses important design decisionsof a data analyst working on food sales prediction, such as the temporal granularity of salesdata, the input variables to use for predicting sales and the representation of the sales outputvariable. In addition, it reviews machine learning algorithms that have been applied to foodsales prediction and appropriate measures for evaluating their accuracy. Finally, it discussesthe main challenges and opportunities for applied machine learning in the domain of foodsales prediction.
