The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research

Abstract 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...

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Autores principales:	Nicholas G. Reich, Matthew Cornell, Evan L. Ray, Katie House, Khoa Le
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/be7aeb3e86c243b7b92bcddfd06f1baa
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spelling	oai:doaj.org-article:be7aeb3e86c243b7b92bcddfd06f1baa2021-12-02T14:11:33ZThe Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research10.1038/s41597-021-00839-52052-4463https://doaj.org/article/be7aeb3e86c243b7b92bcddfd06f1baa2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41597-021-00839-5https://doaj.org/toc/2052-4463Abstract 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.Nicholas G. ReichMatthew CornellEvan L. RayKatie HouseKhoa LeNature PortfolioarticleScienceQENScientific Data, Vol 8, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Nicholas G. Reich Matthew Cornell Evan L. Ray Katie House Khoa Le The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
description	Abstract 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.
format	article
author	Nicholas G. Reich Matthew Cornell Evan L. Ray Katie House Khoa Le
author_facet	Nicholas G. Reich Matthew Cornell Evan L. Ray Katie House Khoa Le
author_sort	Nicholas G. Reich
title	The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
title_short	The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
title_full	The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
title_fullStr	The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
title_full_unstemmed	The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
title_sort	zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/be7aeb3e86c243b7b92bcddfd06f1baa
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The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research

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