  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.
