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Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

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  • Emir Zunic
  • Kemal Korjenic
  • Kerim Hodzic
  • Dzenana Donko

Abstract

This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.

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  • Emir Zunic & Kemal Korjenic & Kerim Hodzic & Dzenana Donko, 2020. "Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data," Papers 2005.07575, arXiv.org.
    • Handle: RePEc:arx:papers:2005.07575
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    References listed on IDEAS

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    1. Serkan Aras & İpek Deveci Kocakoç & Cigdem Polat, 2017. "Comparative study on retail sales forecasting between single and combination methods," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(5), pages 803-832, September.
    2. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    3. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    4. Au, Kin-Fan & Choi, Tsan-Ming & Yu, Yong, 2008. "Fashion retail forecasting by evolutionary neural networks," International Journal of Production Economics, Elsevier, vol. 114(2), pages 615-630, August.
    5. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    6. Emir Žunić & Dženana Đonko & Emir Buza, 2020. "An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics," Complexity, Hindawi, vol. 2020, pages 1-24, January.
    7. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
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    Cited by:

    1. Konstantinos P. Fourkiotis & Athanasios Tsadiras, 2024. "Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions," Forecasting, MDPI, vol. 6(1), pages 1-17, February.

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