论文标题
到处都是层次结构 - 管理和衡量分层时间序列中的不确定性
Hierarchies Everywhere -- Managing & Measuring Uncertainty in Hierarchical Time Series
论文作者
论文摘要
我们研究了通过行为/贝叶斯镜头对大量相关时间序列进行调和的预测的问题。我们的方法明确承认并利用了该系列的“连接性”，从时间序列特征和预测准确性以及层次结构方面。通过最大程度地利用可用信息，并通过显着降低了层次预测问题的维度，我们展示了如何提高调和预测的准确性。与现有方法相反，我们的结构允许分析和评估每个层次级别添加的预测值。我们的调和预测是固有的概率，无论是否使用概率基础预测。
We examine the problem of making reconciled forecasts of large collections of related time series through a behavioural/Bayesian lens. Our approach explicitly acknowledges and exploits the 'connectedness' of the series in terms of time-series characteristics and forecast accuracy as well as hierarchical structure. By making maximal use of the available information, and by significantly reducing the dimensionality of the hierarchical forecasting problem, we show how to improve the accuracy of the reconciled forecasts. In contrast to existing approaches, our structure allows the analysis and assessment of the forecast value added at each hierarchical level. Our reconciled forecasts are inherently probabilistic, whether probabilistic base forecasts are used or not.
