大规模零售需求预测中保持准确性的稳定性正则化
Accuracy-Preserving Stability Regularization for Large-Scale Retail Demand Forecasting
AI总结：
研究大规模零售需求预测，提出在训练时对连续序列内变化施加惩罚的方法，结合多种特征评估稳定性。实验表明稳定性感知混合模型能提高预测稳定性得分，且RMSE变化小，拓展了零售预测从点误差最小化到准确性-稳定性权衡的评估视角。
AI中文摘要：
零售需求预测在补货、产能、劳动力和运输规划周期中反复使用。点误差目标无法约束相邻预测之间的突然变化，事后平滑仅在模型拟合后起作用。我们探讨在训练时对连续的序列内变化施加惩罚，能否在不大幅改变点预测准确性的情况下提高水平预测路径的稳定性。该惩罚在一个时间结构化的管道中进行评估，该管道结合了近期需求嵌入与日历、价格、层次结构、商品和商店特征。在选定的1000、3000和4000序列规模的M5需求序列上，稳定性感知混合模型的预测稳定性得分分别比XGBoost提高了6.91%、6.66%和7.68%，而在三个随机种子下RMSE变化保持在0.72%以内。事后指数平滑的原始变化较低，但RMSE成本较高；训练时正则化保留了更多的点预测准确性，在归一化稳定性下表现良好。这些发现将预测评估从点误差最小化扩展到运营零售预测的准确性-稳定性权衡视角。
英文摘要：
Retail demand forecasts are reused across replenishment, capacity, labor, and transportation planning cycles. Point-error objectives do not constrain abrupt movement between adjacent forecasts, while post-hoc smoothing acts only after model fitting. We ask whether a training-time penalty on consecutive within-series movement can improve horizontal forecast-path stability without materially changing point accuracy. The penalty is evaluated in a temporal-structured pipeline combining recent-demand embeddings with calendar, price, hierarchy, item, and store features. On selected M5 demand series at 1000, 3000, and 4000-series scales, the stability-aware hybrid model improves Forecast Stability Score over XGBoost by 6.91%, 6.66%, and 7.68%, respectively, while RMSE changes remain within 0.72% across three random seeds. Post-hoc exponential smoothing attains lower raw movement but incurs a larger RMSE cost; training-time regularization preserves more point accuracy and performs favorably under normalized stability. These findings extend forecast evaluation from point-error minimization toward an accuracy-stability trade-off perspective for operational retail forecasting.
