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Automatic Interpretable Retail forecasting with promotional scenarios

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  • Gür Ali, Özden
  • Gürlek, Ragıp

Abstract

Budgeting and planning processes require medium-term sales forecasts with marketing scenarios. The complexity in modern retailing necessitates consistent, automatic forecasting and insight generation. Remedies to the high dimensionality problem have drawbacks; black box machine learning methods require voluminous data and lack insights, while regularization may bias causal estimates in interpretable models.

Suggested Citation

  • Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    • Handle: RePEc:eee:intfor:v:36:y:2020:i:4:p:1389-1406
    DOI: 10.1016/j.ijforecast.2020.02.003
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    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Harald J. van Heerde & Peter S. H. Leeflang & Dick R. Wittink, 2002. "How Promotions Work: Scan Pro-Based Evolutionary Model Building," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 198-220, July.
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    4. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    5. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    6. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218.
    7. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
    8. Gur Ali, Ozden & Pinar, Efe, 2016. "Multi-period-ahead forecasting with residual extrapolation and information sharing — Utilizing a multitude of retail series," International Journal of Forecasting, Elsevier, vol. 32(2), pages 502-517.
    9. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    10. Lee G. Cooper & Giovanni Giuffrida, 2000. "Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results," Management Science, INFORMS, vol. 46(2), pages 249-264, February.
    11. Ailawadi, Kusum L. & Beauchamp, J.P. & Donthu, Naveen & Gauri, Dinesh K. & Shankar, Venkatesh, 2009. "Communication and Promotion Decisions in Retailing: A Review and Directions for Future Research," Journal of Retailing, Elsevier, vol. 85(1), pages 42-55.
    12. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    13. Jing Zeng, 2017. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 74-90, January.
    14. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737.
    15. Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
    16. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    17. Gür Ali, Özden & Yaman, Kübra, 2013. "Selecting rows and columns for training support vector regression models with large retail datasets," European Journal of Operational Research, Elsevier, vol. 226(3), pages 471-480.
    18. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    19. Martin Natter & Thomas Reutterer & Andreas Mild & Alfred Taudes, 2007. "—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing," Marketing Science, INFORMS, vol. 26(4), pages 576-583, 07-08.
    20. Hsu, Nan-Jung & Hung, Hung-Lin & Chang, Ya-Mei, 2008. "Subset selection for vector autoregressive processes using Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3645-3657, March.
    21. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    22. Bandyopadhyay, Subir, 2009. "A Dynamic Model of Cross-Category Competition: Theory, Tests and Applications," Journal of Retailing, Elsevier, vol. 85(4), pages 468-479.
    23. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    24. Trapero, Juan R. & Pedregal, Diego J. & Fildes, R. & Kourentzes, N., 2013. "Analysis of judgmental adjustments in the presence of promotions," International Journal of Forecasting, Elsevier, vol. 29(2), pages 234-243.
    25. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    26. Ma, Shaohui & Fildes, Robert, 2017. "A retail store SKU promotions optimization model for category multi-period profit maximization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 680-692.
    27. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    28. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    29. Peter Ebbes & Dominik Papies & Harald J. van Heerde, 2011. "The Sense and Non-Sense of Holdout Sample Validation in the Presence of Endogeneity," Marketing Science, INFORMS, vol. 30(6), pages 1115-1122, November.
    30. 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.
    31. Ruomeng Cui & Santiago Gallino & Antonio Moreno & Dennis J. Zhang, 2018. "The Operational Value of Social Media Information," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1749-1769, October.
    32. Lee G. Cooper & Penny Baron & Wayne Levy & Michael Swisher & Paris Gogos, 1999. "PromoCast™: A New Forecasting Method for Promotion Planning," Marketing Science, INFORMS, vol. 18(3), pages 301-316.
    33. Moshe Givon & Dan Horsky, 1990. "Untangling the Effects of Purchase Reinforcement and Advertising Carryover," Marketing Science, INFORMS, vol. 9(2), pages 171-187.
    34. Pfaff, Bernhard, 2008. "VAR, SVAR and SVEC Models: Implementation Within R Package vars," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i04).
    35. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    36. Özden Gür Ali, 2013. "Driver Moderator Method For Retail Sales Prediction," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(06), pages 1261-1286.
    37. Fildes, Robert & Goodwin, Paul & Önkal, Dilek, 2019. "Use and misuse of information in supply chain forecasting of promotion effects," International Journal of Forecasting, Elsevier, vol. 35(1), pages 144-156.
    38. Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
    39. Suresh Divakar & Brian T. Ratchford & Venkatesh Shankar, 2005. "Practice Prize Article—: A Multichannel, Multiregion Sales Forecasting Model and Decision Support System for Consumer Packaged Goods," Marketing Science, INFORMS, vol. 24(3), pages 334-350, July.
    40. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    41. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    42. Peter S.H. Leeflang & Harald J. van Heerde & Dick Wittink, 2002. "How Promotions Work: SCAN*PRO-Based Evolutionary Model Building," Yale School of Management Working Papers ysm292, Yale School of Management.
    43. Andrews, Rick L. & Currim, Imran S. & Leeflang, Peter & Lim, Jooseop, 2008. "Estimating the SCAN⁎PRO model of store sales: HB, FM or just OLS?," International Journal of Research in Marketing, Elsevier, vol. 25(1), pages 22-33.
    44. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    45. Stephan Kolassa, 2016. "Sometimes It's Better to Be Simple than Correct," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 40, pages 20-26, Winter.
    46. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    47. Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July.
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    3. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).

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