  Two major challenges in demand forecasting are product cannibalization and
long term forecasting. Product cannibalization is a phenomenon in which high
demand of some products leads to reduction in sales of other products. Long
term forecasting involves forecasting the sales over longer time frame that is
critical for strategic business purposes. Also, conventional methods, for
instance, recurrent neural networks may be ineffective where train data size is
small as in the case in this study. This work presents XGBoost-based
three-stage framework that addresses product cannibalization and associated
long term error propagation problems. The performance of the proposed
three-stage XGBoost-based framework is compared to and is found superior than
that of regular XGBoost algorithm.
