论文标题
Dirichlet比例模型用于层次相干概率预测
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting
论文作者
论文摘要
在许多实际预测应用中，概率，层次相干的预测是一个关键问题 - 目标是为在预先指定的树层次结构中安排的大量时间序列获得相干概率预测。在本文中，我们提出了一个端到端的深层概率模型，用于层次预测，该模型是由经典自上而下策略激发的。它共同了解了根时间序列的分布，并且（dirichlet）比例根据其在任何时间点之间在其子女中分配的（dirichlet）比例。由此产生的预测自然是连贯的，并提供了层次结构中所有时间序列的概率预测。我们在几个公共数据集上进行了实验，与最先进的基线相比，大多数数据集的显着改善高达26％。最后，与更传统的自下而上的建模相比，我们还为我们自上而下的方法的优越性提供了理论上的理由。
Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a large number of time series arranged in a pre-specified tree hierarchy. In this paper, we present an end-to-end deep probabilistic model for hierarchical forecasting that is motivated by a classical top-down strategy. It jointly learns the distribution of the root time series, and the (dirichlet) proportions according to which each parent time-series is split among its children at any point in time. The resulting forecasts are naturally coherent, and provide probabilistic predictions over all time series in the hierarchy. We experiment on several public datasets and demonstrate significant improvements of up to 26% on most datasets compared to state-of-the-art baselines. Finally, we also provide theoretical justification for the superiority of our top-down approach compared to the more traditional bottom-up modeling.
