Predictability limit of partially observed systems

Abstract Applications from finance to epidemiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only partially observed. We demonstrate that a system’s predictability degrades as a function of temporal sampling, regardless of the adopted forecasting model. We q...

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Autores principales:	Andrés Abeliuk, Zhishen Huang, Emilio Ferrara, Kristina Lerman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/89a8ba139d4b4b4e9e2e92ddd36a2488
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spelling	oai:doaj.org-article:89a8ba139d4b4b4e9e2e92ddd36a24882021-12-02T15:09:40ZPredictability limit of partially observed systems10.1038/s41598-020-77091-12045-2322https://doaj.org/article/89a8ba139d4b4b4e9e2e92ddd36a24882020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77091-1https://doaj.org/toc/2045-2322Abstract Applications from finance to epidemiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only partially observed. We demonstrate that a system’s predictability degrades as a function of temporal sampling, regardless of the adopted forecasting model. We quantify the loss of predictability due to sampling, and show that it cannot be recovered by using external signals. We validate the generality of our theoretical findings in real-world partially observed systems representing infectious disease outbreaks, online discussions, and software development projects. On a variety of prediction tasks—forecasting new infections, the popularity of topics in online discussions, or interest in cryptocurrency projects—predictability irrecoverably decays as a function of sampling, unveiling predictability limits in partially observed systems.Andrés AbeliukZhishen HuangEmilio FerraraKristina LermanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andrés Abeliuk Zhishen Huang Emilio Ferrara Kristina Lerman Predictability limit of partially observed systems
description	Abstract Applications from finance to epidemiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only partially observed. We demonstrate that a system’s predictability degrades as a function of temporal sampling, regardless of the adopted forecasting model. We quantify the loss of predictability due to sampling, and show that it cannot be recovered by using external signals. We validate the generality of our theoretical findings in real-world partially observed systems representing infectious disease outbreaks, online discussions, and software development projects. On a variety of prediction tasks—forecasting new infections, the popularity of topics in online discussions, or interest in cryptocurrency projects—predictability irrecoverably decays as a function of sampling, unveiling predictability limits in partially observed systems.
format	article
author	Andrés Abeliuk Zhishen Huang Emilio Ferrara Kristina Lerman
author_facet	Andrés Abeliuk Zhishen Huang Emilio Ferrara Kristina Lerman
author_sort	Andrés Abeliuk
title	Predictability limit of partially observed systems
title_short	Predictability limit of partially observed systems
title_full	Predictability limit of partially observed systems
title_fullStr	Predictability limit of partially observed systems
title_full_unstemmed	Predictability limit of partially observed systems
title_sort	predictability limit of partially observed systems
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/89a8ba139d4b4b4e9e2e92ddd36a2488
work_keys_str_mv	AT andresabeliuk predictabilitylimitofpartiallyobservedsystems AT zhishenhuang predictabilitylimitofpartiallyobservedsystems AT emilioferrara predictabilitylimitofpartiallyobservedsystems AT kristinalerman predictabilitylimitofpartiallyobservedsystems
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Predictability limit of partially observed systems

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