An XGBoost-Based Forecasting Framework for Product Cannibalization
It addresses demand forecasting challenges for businesses, but appears incremental as it builds on existing XGBoost methods.
The paper tackles product cannibalization and long-term forecasting in demand forecasting by proposing a three-stage XGBoost-based framework, which outperforms regular XGBoost.
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.