A Bayesian Model for Forecasting Hierarchically Structured Time Series
A Bayesian Model for Forecasting Hierarchically Structured Time Series
复制标题
预测层次结构时间序列的贝叶斯模型
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
B. Garcia
中科院分区：
文献类型：
--
作者：
Julie Novak;S. Mcgarvie;B. Garcia
任何大型组织的一项重要任务是准备关键绩效指标的预测。通常，这些组织是以层级方式构建的，并且出于操作原因，这些指标的预测可能已经由专注于业务中某些领域的专家在层级的每个级别上彼此独立地获得。不能保证这些总量在合并后将与直接在层次结构的其他各级产生的预测一致。我们提出了一个贝叶斯分层方法，将初始预测作为观测数据，然后将其与先验信息和历史预测精度相结合，以推断修订后的预测的概率分布。当用于创建点估计值时，此方法可以反映对层次结构中特定级别的更高精度的偏好。我们提出了模拟和真实的数据研究，以证明当我们的方法在改进的推论替代方法的结果。
An important task for any large-scale organization is to prepare forecasts of key performance metrics. Often these organizations are structured in a hierarchical manner and for operational reasons, projections of these metrics may have been obtained independently from one another at each level of the hierarchy by specialists focusing on certain areas within the business. There is no guarantee that when combined, these aggregates will be consistent with projections produced directly at other levels of the hierarchy. We propose a Bayesian hierarchical method that treats the initial forecasts as observed data which are then combined with prior information and historical predictive accuracy to infer a probability distribution of revised forecasts. When used to create point estimates, this method can reflect preferences for increased accuracy at specific levels in the hierarchy. We present simulated and real data studies to demonstrate when our approach results in improved inferences over alternative methods.