Forecast reconciliation is an important research topic. Yet, there is currently neither formal framework nor practical method for the probabilistic reconciliation of count time series. In this paper we propose a definition of coherency and reconciled probabilistic forecast which applies to both real-valued and count variables and a novel method for probabilistic reconciliation. It is based on a generalization of Bayes' rule and it can reconcile both real-value and count variables. When applied to count variables, it yields a reconciled probability mass function. Our experiments with the temporal reconciliation of count variables show a major forecast improvement compared to the probabilistic Gaussian reconciliation.

翻译：预测协调是一个重要的研究课题。然而，目前既没有正式框架，也没有实用方法用于计数时间序列的概率协调。本文提出了适用于实值变量和计数变量的协调性与协调概率预测的定义，以及一种新颖的概率协调方法。该方法基于贝叶斯规则的推广，可同时协调实值变量和计数变量。当应用于计数变量时，它能够生成协调的概率质量函数。我们针对计数变量的时间协调实验表明，与概率高斯协调相比，该方法显著提升了预测效果。