Forecasting has emerged as an important component of informed, data-driven decision-making in a wide array of fields. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. This framework clearly defines the constituent parts of a probabilistic forecast and proposes one approach for representing these data elements. The data model is implemented in Zoltar, a new software application that stores forecasts using the data model and provides standardized API access to the data. In one real-time case study, an instance of the Zoltar web application was used to store, provide access to, and evaluate real-time forecast data on the order of 10⁸ rows, provided by over 40 international research teams from academia and industry making forecasts of the COVID-19 outbreak in the US. Tools and data infrastructure for probabilistic forecasts, such as those introduced here, will play an increasingly important role in ensuring that future forecasting research adheres to a strict set of rigorous and reproducible standards.

预测已成为广泛领域中以数据为依据的明智决策的重要组成部分。我们为概率预测引入了一个新的数据模型，该模型包含了广泛的预测设置。该框架明确定义了概率预测的组成部分，并提出了一种表示这些数据元素的方法。数据模型在Zoltar中实现，Zoltar是一种新的软件应用程序，它使用数据模型存储预测并提供对数据的标准化API访问。在一个实时案例研究中，使用Zoltar Web应用程序的一个实例来存储，提供访问和评估实时预测数据，其数量级为10 ⁸由来自学术界和行业的40多个国际研究团队提供的数据行，对美国COVID-19的爆发做出了预测。概率预测的工具和数据基础结构（例如此处介绍的那些工具）将在确保未来的预测研究遵循一组严格且可重复的标准方面发挥越来越重要的作用。