FWMAE_Citation_Notice_PKapadia.pdf

The FWMAE (Floor-adjusted Weighted Mean Absolute Error) is a forecasting evaluation metric developed by Prinal Kapadia as part of his Doctoral research on demand forecasting for long lead-time finished goods using Bayesian Deep Learning. This metric introduces a novel weighting and stabilization method for evaluating forecast error in volatile, low-demand, or intermittent demand environments.

带下限调整的加权平均绝对误差（Floor-adjusted Weighted Mean Absolute Error, FWMAE）是普里纳尔·卡帕迪亚（Prinal Kapadia）在其利用贝叶斯深度学习开展长交付周期产成品需求预测的博士研究中提出的一种预测评估指标。该指标针对波动性、低需求或间歇性需求场景下的预测误差评估，提出了一种创新的加权与稳定化方法。