论文标题
比较了美国的训练有素且未经训练的概率合奏预测，对美国的Covid-19案件和死亡人数
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
论文作者
论文摘要
美国COVID-19预测中心集合骨料预测了许多贡献团队在美国，美国Covid-19的短期负担。我们研究建立合奏的方法，该合奏结合了这些团队的预测。这些实验已告知轮毂使用的集合方法。为了对决策者最有用，合奏预测必须在组件预测的两个关键特征的情况下具有稳定的性能：（1）偶尔与报告的数据发生偶然的未对准，以及（2）随着时间的推移，组件预报者的相对性能的相对性能。我们的结果表明，在存在这些挑战的情况下，使用所有组件预测的同样加权中位数的未经训练和强大的方法是支持公共卫生决策者的好选择。在某些贡献预测者具有良好性能的稳定记录的环境中，训练有素的合奏使这些预报员的体重更高也可能会有所帮助。
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 policy makers, 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.
