Data Aggregation and Demand Prediction
References
- (2017) Leveraging comparables for new product sales forecasting. Preprint, submitted September 30, https://dx.doi.org/10.2139/ssrn.3086237.Google Scholar
- (2019) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.Abstract, Google Scholar
- (2019) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
- (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7):1145–1159.Crossref, Google Scholar
- (2010) Inventory management of a fast-fashion retail network. Oper. Res. 58(2):257–273.Link, Google Scholar
- (2020) Designing the right global supply chain network. Manufacturing Service Oper. Management 22(1):15–24.Link, Google Scholar
- (2021) Promotion optimization for multiple items in supermarkets. Management Sci. 67(4):2340–2364.Link, Google Scholar
- (2022) Demand Prediction in Retail: A Practical Guide to Leverage Data and Predictive Analytics (Springer, Berlin).Crossref, Google Scholar
- (2017) The impact of linear optimization on promotion planning. Oper. Res. 65(2):446–468.Link, Google Scholar
- (2000) Turning datamining into a management science tool: New algorithms and empirical results. Management Sci. 46(2):249–264.Link, Google Scholar
- (1999) PromocastTM: A new forecasting method for promotion planning. Marketing Sci. 18(3):301–316.Link, Google Scholar
- (2004) How to use aggregation and combined forecasting to improve seasonal demand forecasts. Internat. J. Production Econom. 90(2):151–167.Crossref, Google Scholar
- (2017) Task-based end-to-end model learning in stochastic optimization. Adv. Neural Inform. Processing Systems 30:5484–5494. Google Scholar
- (2021) Smart “predict, then optimize.” Management Sci. 68(1):9–26. Google Scholar
- (1985) Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models. Ann. Statist. 13(1):342–368.Crossref, Google Scholar
- (2018) How research in production and operations management may evolve in the era of big data. Production Oper. Management 27(9):1670–1684.Crossref, Google Scholar
- (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
- (2019a) Use and misuse of information in supply chain forecasting of promotion effects. Internat. J. Forecasting 35(1):144–156.Crossref, Google Scholar
- (2019b) Retail forecasting: Research and practice. Internat. J. Forecasting.Crossref, Google Scholar
- (2003) Econometric Analysis (Pearson Education India).Google Scholar
- (2013) Driver moderator method for retail sales prediction. Internat. J. Inform. Tech. Decision Making 12(06):1261–1286.Crossref, Google Scholar
- , Say S, Van Woensel T, Fransoo J (2009) SKU demand forecasting in the presence of promotions. Expert Systems Appl. 36(10):12340–12348.Crossref, Google Scholar
- (2019) Statistical Learning with Sparsity: The Lasso and Generalizations (CRC Press, Boca Raton, FL).Google Scholar
- (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2 (Springer, Berlin).Crossref, Google Scholar
- (2019) Forecasting new product life cycle curves: Practical approach and empirical analysis. Manufacturing Service Oper. Management 21(1):66–85.Link, Google Scholar
- (2014) The value of competitive information in forecasting FMCG retail product sales and the variable selection problem. Eur. J. Oper. Res. 237(2):738–748.Crossref, Google Scholar
- (2019) Forecasting retailer product sales in the presence of structural change. Eur. J. Oper. Res. 279(2):459–470.Crossref, Google Scholar
- (2018) A model-based embedding technique for segmenting customers. Oper. Res. 66(5):1247–1267.Link, Google Scholar
- (2010) Do inventory and gross margin data improve sales forecasts for us public retailers? Management Sci. 56(9):1519–1533.Link, Google Scholar
- (2007) Demand estimation and assortment optimization under substitution: Methodology and application. Oper. Res. 55(6):1001–1021.Link, Google Scholar
- (2017) Provably optimal algorithms for generalized linear contextual bandits. Internat. Conf.on Machine Learn., 2071–2080.Google Scholar
- (2021) On-time last-mile delivery: Order assignment with travel-time predictors. Management Sci. 67(7):4095–4119.Link, Google Scholar
- (2016) Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information. Eur. J. Oper. Res. 249(1):245–257.Crossref, Google Scholar
- (2004) The determinants of pre- and postpromotion dips in sales of frequently purchased goods. J. Marketing Res. 41(3):339–350.Crossref, Google Scholar
- (1967) Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Sympos. on Math. Statist. and Probability, vol. 1, 281–297.Google Scholar
- (2019) Generalized Linear Models (Routledge, Oxfordshire, England).Crossref, Google Scholar
- (2017) Algorithms for generalized clusterwise linear regression. INFORMS J. Comput. 29(2):301–317.Link, Google Scholar
- (2016) Fast and flexible ADMM algorithms for trend filtering. J. Comput. Graphical Statist. 25(3):839–858.Crossref, Google Scholar
- (1971) Objective criteria for the evaluation of clustering methods. J. Amer. Statist. Assoc. 66(336):846–850.Crossref, Google Scholar
- (2015) High dimensional statistics lecture notes. Unpublished notes.Google Scholar
- (1995) Multiple hypothesis testing. Annu. Rev. Psych. 46(1):561–584.Crossref, Google Scholar
- (2018) Forecasting at scale. Amer. Statist. 72(1):37–45.Crossref, Google Scholar
- (1996) Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. B 58(1):267–288.Crossref, Google Scholar
- (2011) The solution path of the generalized lasso. Ann. Statist. 39(3):1335–1371.Crossref, Google Scholar
- (2005) Sparsity and smoothness via the fused lasso. J. Royal Statist. Soc. Ser. B Statist. Methodology 67(1):91–108.Crossref, Google Scholar
- (2019) A prescriptive analytics approach to markdown pricing for an e-commerce retailer. J. Pattern Recognition Res. 14(1):1–20.Google Scholar
- (2000) The estimation of pre- and postpromotion dips with store-level scanner data. J. Marketing Res. 37(3):383–395.Crossref, Google Scholar
- (2005) Regularization and variable selection via the elastic net. J. Royal Statist. Soc. Ser. B Statist. Methodology 67(2):301–320.Crossref, Google Scholar

