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An application of support vector machines to sales forecasting under promotions

Author

Listed:
  • G. Di Pillo

    (Sapienza University of Rome)

  • V. Latorre

    (Sapienza University of Rome)

  • S. Lucidi

    (Sapienza University of Rome)

  • E. Procacci

    (ACT-OperationsResearch SRL)

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.

Suggested Citation

  • G. Di Pillo & V. Latorre & S. Lucidi & E. Procacci, 2016. "An application of support vector machines to sales forecasting under promotions," 4OR, Springer, vol. 14(3), pages 309-325, September.
    • Handle: RePEc:spr:aqjoor:v:14:y:2016:i:3:d:10.1007_s10288-016-0316-0
    DOI: 10.1007/s10288-016-0316-0
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    References listed on IDEAS

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    1. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    2. Gianni Di Pillo & Vittorio Latorre & Stefano Lucidi & Enrico Procacci, 2013. "An application of learning machines to sales forecasting under promotions," DIAG Technical Reports 2013-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    3. Juan R Trapero & Nikolaos Kourentzes & Robert Fildes, 2015. "On the identification of sales forecasting models in the presence of promotions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 299-307, February.
    4. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
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    Citations

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    Cited by:

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    2. Ma, Shaohui & Fildes, Robert, 2021. "Retail sales forecasting with meta-learning," European Journal of Operational Research, Elsevier, vol. 288(1), pages 111-128.
    3. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    4. Yves Crama & Michel Grabisch & Silvano Martello, 2018. "Sweet sixteen," 4OR, Springer, vol. 16(1), pages 1-13, March.
    5. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    6. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    7. Majd Kharfan & Vicky Wing Kei Chan & Tugba Firdolas Efendigil, 2021. "A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches," Annals of Operations Research, Springer, vol. 303(1), pages 159-174, August.
    8. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.

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