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.
翻译:我们从行为/巴伊西亚角度来研究对大量相关时间序列的大型收集进行调和预测的问题。我们的方法明确承认并利用该系列在时间序列特性和预测准确性以及等级结构方面的“关联性 ” 。我们通过最大限度地利用现有信息,通过大量减少等级预测问题的规模,表明如何提高调和预测的准确性。与现有方法不同,我们的结构允许分析和评估每个等级层次的预测增加值。我们的调和预测具有内在的概率性,无论是否使用概率基础预测。