Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be "smooth" across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the CovidCast dataset as well as a small simulation example.

翻译：自COVID-19大流行开始以来,预测方法一直引起许多注意,并已成为一个特别热门的主题。在本文件中,我们审议了旨在同时预测若干地平线的多期预测问题。我们提出了一个新的办法,将预测强制到跨地平线上,并将其应用于两个任务:通过回归点估算和通过量回归度预测的间隔预测。这种方法是为实时分布的COVID-19预报而开发的。我们用CovidCast数据集和一个小型模拟例子来说明拟议的技术。