Identifying Popular Products at an Early Stage of Sales Season for Apparel Industry

Published Online:https://doi.org/10.1287/inte.2023.0022

References

  • Anderson JA (1995) An Introduction to Neural Networks (MIT Press, Cambridge, MA).Google Scholar
  • Baardman L, Levin I, Perakis G, Singhvi D, et al. (2018) Leveraging comparables for new product sales forecasting. Production Oper. Management 27(12):2340–2343.Google Scholar
  • Box GE, Pierce DA (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Amer. Statist. Assoc. 65(332):1509–1526.Google Scholar
  • Brown RG (1963) Smoothing, Forecasting and Prediction of Discrete Time Series (Prentice-Hall, Upper Saddle River, NJ).Google Scholar
  • Burges CJ (2010) From RankNet to LambdaRank to LambdaMart: An overview. Learning 11(23–581):81.Google Scholar
  • Caro F, Gallien J, Díaz M, García J, Corredoira JM, Montes M, Ramos JA, et al. (2010) Zara uses operations research to reengineer its global distribution process. Interfaces 40(1):71–84.LinkGoogle Scholar
  • Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. Krishnapuram B, Shah M, eds. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), 785–794.Google Scholar
  • Choi TMJ (2016) Information Systems for the Fashion and Apparel Industry (Woodhead Publishing, Sawston, UK).Google Scholar
  • Cortes C, Vapnik V (1995) Support-vector networks. Machine Learn. 20(3):273–297.Google Scholar
  • Fischler MA, Bolles RC (1981) Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6):381–395.Google Scholar
  • Giri C, Thomassey S, Balkow J, Zeng X (2019) Forecasting new apparel sales using deep learning and nonlinear neural network regression. Le Moullec Y, Shi J, eds. Proc. Internat. Conf. Engrg. Sci. and Industrial Appl. (IEEE, Piscataway, NJ), 1–6.Google Scholar
  • Gutierrez RS, Solis AO, Mukhopadhyay S (2008) Lumpy demand forecasting using neural networks. Internat. J. Production Econom. 111(2):409–420.Google Scholar
  • Ho TK (1995) Random decision forests. Abe K, Ebrahimi T, eds. Proc. 3rd Internat. Conf. Document Analysis and Recognition, vol. 1 (IEEE, New York), 278–282.Google Scholar
  • Mantrala MK, Rao S (2001) A decision-support system that helps retailers decide order quantities and markdowns for fashion goods. Interfaces 31(3 supplement):S146–S165.AbstractGoogle Scholar
  • Nenni ME, Giustiniano L, Pirolo L (2013) Demand forecasting in the fashion industry: A review. Internat. J. Engrg. Business Management 5(2013):5–36.Google Scholar
  • Olive DJ (2017) Multiple Linear Regression (Springer, Berlin).Google Scholar
  • Papalexopoulos AD, Hesterberg TC (1990) A regression-based approach to short-term system load forecasting. IEEE Trans. Power Systems 5(4):1535–1547.Google Scholar
  • Singh PK, Gupta Y, Jha N, Rajan A (2019) Fashion retail: Forecasting demand for new items. Preprint, submitted June 27, https://arxiv.org/abs/1907.01960.Google Scholar
  • Smith MA, Côté MJ (2022) Predictive analytics improves sales forecasts for a pop-up retailer. INFORMS J. Appl. Analytics 52(4):379–389.LinkGoogle Scholar
  • Sun ZL, Choi TM, Au KF, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems 46(1):411–419.Google Scholar
  • Sung SW, Jang YJ, Kim JH, Lee J (2017) Business analytics for streamlined assort packing and distribution of fashion goods at kolon sport. Interfaces 47(6):555–573.Google Scholar
  • Thomassey S, Fiordaliso A (2006) A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems 42(1):408–421.Google Scholar
  • Ting KM (2010) Precision and recall. Sammut C, Webb GI, eds. Encyclopedia of Machine Learning (Springer US, Boston), 781–781.Google Scholar
  • Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Management Sci. 6(3):324–342.LinkGoogle Scholar
  • Wong WK, Guo Z (2010) A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Internat. J. Production Econom. 128(2):614–624.Google Scholar
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