A demand forecasting system of product categories defined by their time series using a hybrid approach of ensemble learning with feature engineering

Retail companies face major problems in the estimation of their product's future demand due to the high diversity of sales behavior that each good presents. Different forecasting models are implemented to meet the demand requirements for efficient inventory management. However, in most of the proposed works, a single model approach is applied to forecast all products, ignoring that some methods are better adapted for certain features of the demand time series of each product. The proposed forecasting system addresses this problem, by implementing a two-phase methodology that initially clusters the products with the application of an unsupervised learning approach using the extracted demand features of each good, and then, implements a second phase where, after a feature engineering process, a set of different forecasting methods are evaluated to identify those with best performs for each cluster. Finally, ensemble machine learning models are implemented using the top-performing models of each cluster to carry out the demand estimation. The results indicate that the proposed forecasting system improves the demand estimation over the single forecasting approaches when evaluating the R2, MSE, and MASE quality measures.