论文标题
顺畅的多周期预测，用于预测Covid-19情况
Smooth multi-period forecasting with application to prediction of COVID-19 cases
论文作者
论文摘要
自从19日大流行以来，预测方法一直吸引着很多关注，并已成为一个特别热门的话题。在本文中，我们考虑了旨在一次预测多个视野的多期预测的问题。我们提出了一种新的方法，该方法迫使预测在整个视野中“平滑”，并将其应用于两个任务：通过回归和间隔回归预测的点估计。该方法是针对实时分布式COVID-19的预测开发的。我们使用Covidcast数据集说明了提出的技术以及一个小的仿真示例。
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.
