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.

翻译：需求预测的两大挑战是产品拆解和长期预测。产品拆解是一种现象,某些产品的高需求导致其他产品的销售量减少。长期预测涉及在对战略商业目的至关重要的较长时间范围内预测销售量。此外,常规方法,例如,当培训数据规模小于本研究案例时,经常的神经网络可能无效。这项工作提出了基于XGBst的三阶段框架,以解决产品拆解和相关的长期错误传播问题。拟议的三阶段XGBosost框架的绩效与正常的XGBoost算法相比较,并被认为优于正常的XGBoost算法。