Prophet과 머신러닝 하이브리드 기법을 이용한 Lumpy 수요 예측

초록

This study proposes a machine learning-based hybrid forecasting approach for lumpy demand, which is characterized by intermittency and high variability. The proposed approach adopts Prophet as a baseline forecasting model and improves its predictions through a residual correction mechanism that explicitly accounts for the sign of forecast errors. Residuals are classified by their direction, and separate regression models are trained to correct over- and under-forecasting errors. Experimental results on two datasets show that it achieves substantial improvements under cost structures where over-forecasting penalties are dominant, particularly in comparison with baseline forecasting models. These findings indicate that the proposed hybrid model corrects redisuals in a direction that effectively mitigates the increase in inventory holding costs associated with over-forecasting in lumpy demand environments. Sensitivity analysis further demonstrates that the proposed method provides stable performance across different correction coefficient settings, highlighting its robustness in cost-sensitive inventory environment.

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