论文标题
计数时间序列的概率对帐
Probabilistic Reconciliation of Count Time Series
论文作者
论文摘要
预测和解是一个重要的研究主题。但是，目前既没有正式框架，也没有实用方法来对周期序列的概率对帐。在本文中，我们提出了相干性和调和概率预测的定义，该定义适用于实价和计数变量以及一种概率和解的新方法。它基于贝叶斯规则的概括，它可以调和实价和计数变量。当应用于计数变量时，它会产生一个对帐概率质量函数。与概率高斯和解相比，我们对计数变量的时间对帐进行的实验表明，预测有所改善。
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.
