Smooth Multi-Period Forecasting With Application to Prediction of COVID-19 Cases
提出一种跨预测期平滑的新方法，用于点估计和区间预测，基于CovidCast数据验证，适用于实时分布式疫情预测。
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.