Smooth multi-period forecasting with application to prediction of COVID-19 cases

TL;DR

This paper introduces a novel smooth multi-period forecasting method tailored for COVID-19 case prediction, improving real-time accuracy across multiple horizons through regression and quantile regression techniques.

Contribution

The paper presents a new approach that enforces smoothness across multiple forecast horizons, specifically designed for real-time COVID-19 case prediction.

Findings

Effective in real-time COVID-19 forecasting

Applicable to point and interval predictions

Demonstrated on CovidCast dataset

Abstract

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.