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arXiv · 2202.09723

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

Elena Tuzhilina·Trevor J. Hastie·Daniel J. McDonald·J. Kenneth Tay·Robert Tibshirani
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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.

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Elena Tuzhilina, Trevor J. Hastie, Daniel J. McDonald, J. Kenneth Tay, Robert Tibshirani. 2022-02-20. Smooth multi-period forecasting with application to prediction of COVID-19 cases. https://arxiv.org/abs/2202.09723

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