论文标题
对实现的预测和计算时间序列的预测有效概率对帐
Efficient probabilistic reconciliation of forecasts for real-valued and count time series
论文作者
论文摘要
分层时间序列在几个应用字段中很常见。这些时间序列的预测必须是连贯的，即满足层次结构给出的约束。执行连贯性的最流行技术称为对帐，该技术调整了每个时间序列计算的基本预测。但是，关于概率和解的最新著作提出了一些局限性。在本文中，我们提出了一种基于条件的新方法，以调和任何类型的预测分布。然后，我们引入了一种新算法，称为自下而上的重要性抽样，以有效地从对帐分布中进行采样。它可用于任何基本预测分布：离散，连续或以样本的形式使用，与当前方法相比提供了主要的加速。几个时间层次结构的实验表明，基础概率预测有显着改善。
Hierarchical time series are common in several applied fields. The forecasts for these time series are required to be coherent, that is, to satisfy the constraints given by the hierarchy. The most popular technique to enforce coherence is called reconciliation, which adjusts the base forecasts computed for each time series. However, recent works on probabilistic reconciliation present several limitations. In this paper, we propose a new approach based on conditioning to reconcile any type of forecast distribution. We then introduce a new algorithm, called Bottom-Up Importance Sampling, to efficiently sample from the reconciled distribution. It can be used for any base forecast distribution: discrete, continuous, or in the form of samples, providing a major speedup compared to the current methods. Experiments on several temporal hierarchies show a significant improvement over base probabilistic forecasts.
