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:预测调节是一个重要的研究课题。然而,目前既没有正式的框架,也没有实际的方法来进行计算时间序列的概率调节。在本文件中,我们提出了一个适用于实际估价和计算变量的一致和协调一致的概率预测定义,以及一种新型的概率调节方法。它基于对拜斯规则的概括化,可以同时调节实际价值和数值变量。当应用到计算变量时,它产生一个调和概率质量功能。我们关于计算变量的时间调节的实验显示,与概率调节相比,我们预测有重大改进。</s>