用概率预测订单履约时间，更准且能识别延迟
Conformal Predictive Distributions for Order Fulfillment Time Forecasting
- 用校准预测系统+交叉Venn-Abers方法，保证预测可靠性
- 点预测准确率比旧规则系统高14%，晚到识别率提升75%
- 适合电商物流需精准预估交付时间的场景
准确估计订单履约时间对电商物流至关重要，但传统基于规则的方法难以捕捉配送过程中的固有不确定性。本文提出一种新型分布预测框架，利用校准预测系统（Conformal Predictive Systems）与交叉Venn-Abers预测器——两种模型无关的技术，提供严格的覆盖性或有效性保证。所提机器学习方法融合细粒度时空特征，捕捉履约地点与承运商绩效动态，提升预测精度。此外，设计了一种成本敏感决策规则，将概率预测转化为可靠点预测。在大规模工业数据集上的实验表明，该方法生成具有竞争力的分布预测，且基于机器学习的点预测显著优于现有规则系统：预测准确率最高提升14%，晚到订单识别率最高提升75%。
原文摘要 · Abstract (English)
Accurate estimation of order fulfillment time is critical for e-commerce logistics, yet traditional rule-based approaches often fail to capture the inherent uncertainties in delivery operations. This paper introduces a novel framework for distributional forecasting of order fulfillment time, leveraging Conformal Predictive Systems and Cross Venn-Abers Predictors -- model-agnostic techniques that provide rigorous coverage or validity guarantees. The proposed machine learning methods integrate granular spatiotemporal features, capturing fulfillment location and carrier performance dynamics to enhance predictive accuracy. Additionally, a cost-sensitive decision rule is developed to convert probabilistic forecasts into reliable point predictions. Experimental evaluation on a large-scale industrial dataset demonstrates that the proposed methods generate competitive distributional forecasts, while machine learning-based point predictions significantly outperform the existing rule-based system -- achieving up to 14% higher prediction accuracy and up to 75% improvement in identifying late deliveries.
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