Logo PTI Logo FedCSIS

Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Demand forecasting in the fashion business — an example of customized nearest neighbour and linear mixed model approaches

, , , ,

DOI: http://dx.doi.org/10.15439/2022F256

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 6165 ()

Full text

Abstract. The fashion industry is characterised by the need to make demand forecasts in advance and for highly volatile products for which we often have no sales history at the time the forecasts are made. For this reason, it is necessary to propose forecast mechanisms that can cope with the given conditions. Such forecasts can be based on expert predictions for generalized product categories. In this case, the task of machine learning forecasting methods would be to divide the aggregate prediction into forecasts for individual products, in each colour and size. In the paper, we present several approaches to this specific task. We present the use of the naive method, custom nearest neighbour approach, parametric linear mixed model and an ensemble approach. Overall, the best results we obtained for the ensemble method. Our research was based on real data from fashion retail.


  1. M. Z. Babai, J. E. Boylan, and B. Rostami-Tabar, “Demand forecasting in supply chains: a review of aggregation and hierarchical approaches,” International Journal of Production Research, vol. 60, no. 1, pp. 324–348, 2022.
  2. E. Hofmann and E. Rutschmann, “Big data analytics and demand forecasting in supply chains: a conceptual analysis,” The International Journal of Logistics Management, 2018.
  3. M. Seyedan and F. Mafakheri, “Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities,” Journal of Big Data, vol. 7, no. 1, pp. 1–22, 2020.
  4. A. L. Loureiro, V. L. Miguéis, and L. F. da Silva, “Exploring the use of deep neural networks for sales forecasting in fashion retail,” Decision Support Systems, vol. 114, pp. 81–93, oct 2018. http://dx.doi.org/10.1016/j.dss.2018.08.010
  5. S. Sajja, N. Aggarwal, S. Mukherjee, K. Manglik, S. Dwivedi, and V. Raykar, “Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail,” in ACM International Conference Proceeding Series, 2020. http://dx.doi.org/10.1145/3430984.3430995. ISBN 9781450388177 pp. 281–289.
  6. T. M. Choi, C. L. Hui, N. Liu, S. F. Ng, and Y. Yu, “Fast fashion sales forecasting with limited data and time,” Decision Support Systems, vol. 59, no. 1, pp. 84–92, mar 2014. http://dx.doi.org/10.1016/j.dss.2013.10.008
  7. M. Xia, Y. Zhang, L. Weng, and X. Ye, “Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs,” Knowledge-Based Systems, vol. 36, pp. 253–259, dec 2012. http://dx.doi.org/10.1016/j.knosys.2012.07.002
  8. “Machine learning in predicting demand for fast-moving consumer goods: An exploratory research,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 737–742, 2019. http://dx.doi.org/10.1016/j.ifacol.2019.11.203
  9. S. J. Taylor and B. Letham, “Forecasting at scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, 2018.
  10. J. Fox, Applied regression analysis and generalized linear models. Sage Publications, 2015.
  11. G. E. Box and D. R. Cox, “An analysis of transformations,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 26, no. 2, pp. 211–243, 1964.