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.
@article{arxiv.2202.09723,
title = {Smooth multi-period forecasting with application to prediction of COVID-19 cases},
author = {Elena Tuzhilina and Trevor J. Hastie and Daniel J. McDonald and J. Kenneth Tay and Robert Tibshirani},
journal= {arXiv preprint arXiv:2202.09723},
year = {2022}
}