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.
翻译:分布预测可以量化预测的不确定性,并提供各种预测情景及其相应的概率估计。准确的分布预测对于规划至关重要,例如在决定生产能力或库存分配时。我们提出了一个实用和稳健的分布预测框架,以基于后测试的靴子陷阱和适应性剩余选择为基础。拟议方法对基本预测模式的选择十分有力,说明了投入共变的不确定性,并放松了剩余物和共变假设之间的独立性。它比典型的靴子陷阱方法减少了63%以上,比内部产品销售数据和M4小时竞争数据的各种最先进的深层次学习方法减少了2%-32%。