An application of support vector machines to sales forecasting under promotions

G. Di Pillo (), V. Latorre, S. Lucidi and E. Procacci
Additional contact information
G. Di Pillo: Sapienza University of Rome
V. Latorre: Sapienza University of Rome
S. Lucidi: Sapienza University of Rome
E. Procacci: ACT-OperationsResearch SRL

4OR, 2016, vol. 14, issue 3, No 4, 309-325

Abstract: 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.

Keywords: Machine learning; Support vector machines; Sales forecasting; Promotion policies; Nonlinear optimization; 62M20; 68T05; 90B05; 90B60; 90C30 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10288-016-0316-0

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