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

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.