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

• Autorzy: Henzel Joanna , Wawrowski Łukasz , Kubina Anna , Sikora Marek , Wróbel Łukasz

Identyfikatory

DOI

10.15439/2022F256

• Języki publikacji: EN

Abstrakty

EN

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.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 61--65
• Opis fizyczny: Bibliogr. 11 poz.

Twórcy

autor

Henzel Joanna

autor

Wawrowski Łukasz

autor

Kubina Anna

autor

Sikora Marek

autor

Wróbel Łukasz