Abstract
This paper deals with sales forecasting of a given commodity in a retail store of large distribution. For many years statistical methods such as ARIMA and Exponential Smoothing have been used to this aim. However the statistical methods could fail if high irregularity of sales are present, as happens for instance in case of promotions, because they are not well suited to model the nonlinear behaviors of the sales process. In recent years new methods based on machine learning are being employed for forecasting applications. A preliminary investigation indicates that methods based on the support vector machine (SVM) are more promising than other machine learning methods for the case considered. The paper assesses the application of SVM to sales forecasting under promotion impacts, compares SVM with other statistical methods, and tackles two real case studies.
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We confirm that this submission complies with all ethical standards of 4OR. In particular we confirm that this work has been partially funded by ACT-OperatiosResearch SRL, contract 602/2010 on “Forecasting by Neural Networks and SVM”.
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This work was partially supported by ACT-OperationsResearch under contract 602/2010 on “Forecasting by Neural Networks and SVM”.
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Di Pillo, G., Latorre, V., Lucidi, S. et al. An application of support vector machines to sales forecasting under promotions. 4OR-Q J Oper Res 14, 309–325 (2016). https://doi.org/10.1007/s10288-016-0316-0
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DOI: https://doi.org/10.1007/s10288-016-0316-0