论文标题
Zoltar预测档案：一种促进跨学科预测研究的标准化和存储的工具
The Zoltar forecast archive: a tool to facilitate standardization and storage of interdisciplinary prediction research
论文作者
论文摘要
预测已成为各种各样的领域中知情，数据驱动决策的重要组成部分。我们介绍了一个新的数据模型，以进行概率预测，该模型包括广泛的预测设置。该框架清楚地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。数据模型是在Zoltar中实现的，Zoltar是一种新的软件应用程序，该应用程序使用数据模型预测，并提供对数据的标准化API访问。在一项实时案例研究中，使用Zoltar Web应用程序的实例存储，提供并评估了10美元$^7 $行的实时预测数据，该数据由学术界的20多个国际研究团队和行业提供，对美国的Covid-19爆发进行预测。概率预测的工具和数据基础架构（例如此处介绍的预测）将在确保未来的预测研究遵守一系列严格和可重复的标准方面发挥越来越重要的作用。
Forecasting has emerged as an important component of informed, data-driven decision-making in a wide array of fields. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. This framework clearly defines the constituent parts of a probabilistic forecast and proposes one approach for representing these data elements. The data model is implemented in Zoltar, a new software application that stores forecasts using the data model and provides standardized API access to the data. In one real-time case study, an instance of the Zoltar web application was used to store, provide access to, and evaluate real-time forecast data on the order of 10$^7$ rows, provided by over 20 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.
