论文标题
具有基于回测的引导程序和自适应剩余选择的稳健非参数分布预测
Robust Nonparametric Distribution Forecast with Backtest-based Bootstrap and Adaptive Residual Selection
论文作者
论文摘要
分布预测可以量化预测不确定性，并及其相应的估计概率提供各种预测场景。准确的分配预测对于计划至关重要 - 例如，在制定生产能力或库存分配决策时。我们提出了一个实用且可靠的分布预测框架，该框架依赖于基于回测的引导程序和自适应剩余选择。所提出的方法是对基础预测模型的选择，占输入协变量周围的不确定性，并放松残留物与协变量假设之间的独立性。与经典的Bootstrap方法相比，与各种有关内部产品销售数据和M4小时竞争数据的最先进的深度学习方法相比，它的绝对覆盖误差降低了63％以上，而2％至32％。
Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is crucial for planning - for example when making production capacity or inventory allocation decisions. We propose a practical and robust distribution forecast framework that relies on backtest-based bootstrap and adaptive residual selection. The proposed approach is robust to the choice of the underlying forecasting model, accounts for uncertainty around the input covariates, and relaxes the independence between residuals and covariates assumption. It reduces the Absolute Coverage Error by more than 63% compared to the classic bootstrap approaches and by 2% - 32% compared to a variety of State-of-the-Art deep learning approaches on in-house product sales data and M4-hourly competition data.
