[论文解读] One Global Model, Many Behaviors: Stockout-Aware Feature Engineering and Dynamic Scaling for Multi-Horizon Retail Demand Forecasting with a Cost-Aware Ordering Policy (VN2 Winner Report)

Bartosz Szabłowski|arXiv (Cornell University)|Jan 26, 2026

Forecasting Techniques and Applications被引用 0

一句话总结

VN2 胜出方案使用两阶段的预测-优化管线：一个全局的多期 CatBoost 预测，带有备货不足感知特征，然后再采用成本感知的订货策略。

ABSTRACT

Inventory planning for retail chains requires translating demand forecasts into ordering decisions, including asymmetric shortages and holding costs. The VN2 Inventory Planning Challenge formalizes this setting as a weekly decision-making cycle with a two-week product delivery lead time, where the total cost is defined as the shortage cost plus the holding cost. This report presents the winning VN2 solution: a two-stage predict-then-optimize pipeline that combines a single global multi-horizon forecasting model with a cost-aware ordering policy. The forecasting model is trained in a global paradigm, jointly using all available time series. A gradient-boosted decision tree (GBDT) model implemented in CatBoost is used as the base learner. The model incorporates stockout-aware feature engineering to address censored demand during out-of-stock periods, per-series scaling to focus learning on time-series patterns rather than absolute levels, and time-based observation weights to reflect shifts in demand patterns. In the decision stage, inventory is projected to the start of the delivery week, and a target stock level is calculated that explicitly trades off shortage and holding costs. Evaluated by the official competition simulation in six rounds, the solution achieved first place by combining a strong global forecasting model with a lightweight cost-aware policy. Although developed for the VN2 setting, the proposed approach can be extended to real-world applications and additional operational constraints.

研究动机与目标

• 将需求预测转化为在缺货与交付时间下的成本感知订货决策以驱动库存规划。
• 提出一种可扩展的全局预测方法，以处理异质、间歇的零售时序数据。
• 将预测转化为鲁棒且可解释的订货决策，权衡短缺和持有成本。
• 证明全局模型加上轻量化策略可以在 VN2 场景中优于单对单系列调优，并可拓展到现实世界约束。

提出的方法

• 阶段1：使用 CatBoost 在一个包含所有门店-产品时间序列的表格表征上训练一个全局的多期需求预测模型（三周时域）。
• 阶段2：应用一个成本感知的订货策略，将库存投射到交付周并以权衡缺货与持有成本为目标来设定库存水平。
• 通过具备备货不足感知的特征工程和每个系列的缩放策略来处理在库 censoring 的需求，以突出模式而非水平。
• 引入时间衰减的观测权重以适应行为转变和最近的需求变化。
• 使用一个直接的多期预测方法，对 h ∈ {1,2,3} 进行预测，避免误差累积并保持与时域相关的建模。
• 展示一个模块化的两阶段管线，将预测与订货决策分离，以提升可扩展性和可解释性。

实验结果

研究问题

• RQ1 在 VN2 类似的环境中，单一全局模型如何学习来自大量时间序列的多样化需求模式？
• RQ2 通过特征工程是否能有效缓解缺货审查，从而改善以预测为驱动的库存决策？
• RQ3 使用三周前瞄准的预测的轻量化、成本感知订货策略，在交付时间和非对称成本下是否能实现更低的总成本表现？
• RQ4 所提出的两阶段框架是否具备可扩展性，并可扩展到 VN2 之外的其他运营约束？

主要发现

• 解决方案通过将强大的全局预测模型与轻量化的成本感知策略相结合，在 VN2 竞赛中获胜。
• 具备缺货感知的特征、每系列缩放以及时间衰减观测权重，提升了对异质序列的预测鲁棒性。
• 预测模型采用直接的三期策略，使用 CatBoost 生成所有序列从 t+1 到 t+3 的预测。
• 每系列缩放降低了高容量序列的主导性，强调时间模式。
• 该方法模块化，且可扩展到具有额外约束的现实世界设置。