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On the identification of sales forecasting models in the presence of promotions

Citations

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Cited by:

  1. Montero-Manso, Pablo & Hyndman, Rob J., 2021. "Principles and algorithms for forecasting groups of time series: Locality and globality," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
  2. Perera, H. Niles & Fahimnia, Behnam, 2024. "Multi-period ordering decisions in the presence of retail promotions," European Journal of Operational Research, Elsevier, vol. 319(3), pages 763-776.
  3. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
  4. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.
  5. Xu, Yongzhuo & Kang, Bingyi, 2025. "A novel model based on graph kernel and S-R score in visibility graph for time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
  6. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
  7. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
  8. Yun Shin Lee & Enno Siemsen, 2017. "Task Decomposition and Newsvendor Decision Making," Management Science, INFORMS, vol. 63(10), pages 3226-3245, October.
  9. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
  10. 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.
  11. Goodwin, Paul & Gönül, M. Sinan & Önkal, Dilek, 2019. "When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions," European Journal of Operational Research, Elsevier, vol. 273(3), pages 992-1004.
  12. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
  13. Sagaert, Yves R. & Kourentzes, Nikolaos, 2025. "Inventory management with leading indicator augmented hierarchical forecasts," Omega, Elsevier, vol. 136(C).
  14. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
  15. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  16. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
  17. Chen, Shan & Zhu, Meizhen & Han, Shuihua & Gupta, Shivam, 2026. "A deep fusion framework for end-to-end multi-product inventory optimization in e-commerce scenarios," International Journal of Production Economics, Elsevier, vol. 291(C).
  18. Oikonomou, Konstantinos & Damigos, Dimitris & Dimitriou, Dimitrios, 2025. "Globality in the metal markets: Leveraging cross-learning to forecast aluminum and copper prices," Resources Policy, Elsevier, vol. 103(C).
  19. Marco Zanotti, 2025. "Do global forecasting models require frequent retraining?," Working Papers 551, University of Milano-Bicocca, Department of Economics.
  20. Fahimnia, Ben & Tan, Tarkan & Tahirov, Nail, 2025. "Service-level anchoring in demand forecasting: The moderating impact of retail promotions and product perishability," International Journal of Forecasting, Elsevier, vol. 41(2), pages 554-570.
  21. Hewage, Harsha Chamara & Perera, H. Niles & De Baets, Shari, 2022. "Forecast adjustments during post-promotional periods," European Journal of Operational Research, Elsevier, vol. 300(2), pages 461-472.
  22. 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.
  23. Trapero, Juan R. & Pedregal, Diego J., 2016. "A novel time-varying bullwhip effect metric: An application to promotional sales," International Journal of Production Economics, Elsevier, vol. 182(C), pages 465-471.
  24. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
  25. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
  26. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
  27. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
  28. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2023. "Shrinkage estimator for exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1351-1365.
  29. Bandara, Kasun & Hewamalage, Hansika & Godahewa, Rakshitha & Gamakumara, Puwasala, 2022. "A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1400-1404.
  30. 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.
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