通过动态调整成本不对称性，实现节点级需求预测优化，年省510万美元。
Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism
- 根据节点成本差异动态调整误差分布，优先选择低成本方案。
- 模型自调节修正幅度，实测年节省510万美元。
- 适合需精准成本控制的供应链与金融预测场景。
本文提出一种基于节点特定成本函数不对称性的预测调整方法。模型将成本不对称性动态融入预测误差概率分布，以偏向成本最低的方案。通过计算节约金额，并依据实际节约效果自调节调整强度，使模型能适应站点特异性条件及未建模因素（如校准误差或宏观经济变化）。实证结果表明，该模型可实现每年510万美元的节约。
原文摘要 · Abstract (English)
This work introduces a methodology to adjust forecasts based on node-specific cost function asymmetry. The proposed model generates savings by dynamically incorporating the cost asymmetry into the forecasting error probability distribution to favor the least expensive scenario. Savings are calculated and a self-regulation mechanism modulates the adjustments magnitude based on the observed savings, enabling the model to adapt to station-specific conditions and unmodeled factors such as calibration errors or shifting macroeconomic dynamics. Finally, empirical results demonstrate the model's ability to achieve \$5.1M annual savings.
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