Abstract
Food sales prediction is concerned with estimating future sales of companies in the food industry, such as supermarkets, groceries, restaurants, bakeries and patisseries. Accurate short-term sales prediction allows companies to minimize stocked and expired products inside stores and at the same time avoid missing sales. This paper reviews existing machine learning approaches for food sales prediction. It discusses important design decisions of a data analyst working on food sales prediction, such as the temporal granularity of sales data, the input variables to use for predicting sales and the representation of the sales output variable. In addition, it reviews machine learning algorithms that have been applied to food sales prediction and appropriate measures for evaluating their accuracy. Finally, it discusses the main challenges and opportunities for applied machine learning in the domain of food sales prediction.
Similar content being viewed by others
References
Aho T, Ženko B, Džeroski S, Elomaa T (2012) Multi-target regression with rule ensembles. J Mach Learn Res 1:1–48
Bakker J, Pechenizkiy M (2009) Food wholesales prediction: What is your baseline? In Foundations of Intelligent Systems. In: Proceedings of 18th International Symposium ISMIS 2009, volume 5722 of Lecture Notes in Computer Science, pp 493–502. Springer,
Chen C-Y, Lee W-I, Kuo H-M, Chen C-W, Kung-Hsing Chen (2010) The study of a forecasting sales model for fresh food. Expert Syst Appl 37(12):7696–7702
Doganis P, Alexandridis A, Patrinos P, Sarimveis H (2006) Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. J Food Eng 75(2):196–204
Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87
Frank E, Hall M (2001) A simple approach to ordinal classification. In: Proceedings of the 12th European Conference on Machine Learning, EMCL ’01, pp 145–156, London, UK, UK. Springer
Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59(1–2):161–205
Liu X, Ichise R (2017) Food sales prediction with meteorological data A case study of a Japanese chain supermarket. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10387 LNCS, pp 93–104
Meulstee P, Pechenizkiy M (2008) Food sales prediction: if only it knew what we know. Proc IEEE Int Conf Data Min Workshops, ICDM Workshops 2008:134–143
Tsymbal A, Pechenizkiy M, Cunningham P, Puuronen S (2008) Dynamic integration of classifiers for handling concept drift. Inf Fus 9(1):56–68
Van der Vorst JGAJ, Beulens AJM, De Wit W, Van Beek P (1998) Supply chain management in food chains: improving performance by reducing uncertainty. Int Trans Oper Res 5:487–499
Žliobaite Indre, Bakker Jorn, Pechenizkiy Mykola (2009) Towards Context Aware Food Sales Prediction. In: 2009 IEEE International Conference on Data Mining Workshops, pp 94–99. IEEE, December
Žliobaite I, Bakker J, Pechenizkiy M (2012) Beating the baseline prediction in food sales: how intelligent an intelligent predictor is? Expert Syst Appl 39(1):806–815. https://doi.org/10.1016/j.eswa.2011.07.078
Acknowledgements
I would like to thank the anonymous reviewers for their constructive comments that helped me improve this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tsoumakas, G. A survey of machine learning techniques for food sales prediction. Artif Intell Rev 52, 441–447 (2019). https://doi.org/10.1007/s10462-018-9637-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-018-9637-z