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Machine-Learning Models for Sales Time Series Forecasting

Author

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  • Bohdan M. Pavlyshenko

    (SoftServe, Inc., 2D Sadova St., 79021 Lviv, Ukraine
    Ivan Franko National University of Lviv, 1, Universytetska St., 79000 Lviv, Ukraine)

Abstract

In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning for sales forecasting. The effect of machine-learning generalization has been considered. This effect can be used to make sales predictions when there is a small amount of historical data for specific sales time series in the case when a new product or store is launched. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking techniques, we can improve the performance of predictive models for sales time series forecasting.

Suggested Citation

  • Bohdan M. Pavlyshenko, 2019. "Machine-Learning Models for Sales Time Series Forecasting," Data, MDPI, vol. 4(1), pages 1-11, January.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:1:p:15-:d:198898
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    References listed on IDEAS

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    Cited by:

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    3. Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
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    7. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.

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