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Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

2022; Elsevier BV; Volume: 39; Issue: 3 Linguagem: Inglês

10.1016/j.ijforecast.2022.06.005

1872-8200

Evan L Ray, Logan Brooks, Jacob Bien, Matthew Biggerstaff, Nikos I Bosse, Johannes Bracher, Estee Y. Cramer, Sebastian Funk, Aaron Gerding, Michael A. Johansson, Aaron Rumack, Yijin Wang, Martha Zorn, Ryan J. Tibshirani, Nicholas G Reich,

Forecasting Techniques and Applications

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.