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Predictability limit of partially observed systems
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 t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687903/ https://www.ncbi.nlm.nih.gov/pubmed/33235260 http://dx.doi.org/10.1038/s41598-020-77091-1 |
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author | Abeliuk, Andrés Huang, Zhishen Ferrara, Emilio Lerman, Kristina |
author_facet | Abeliuk, Andrés Huang, Zhishen Ferrara, Emilio Lerman, Kristina |
author_sort | Abeliuk, Andrés |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-7687903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76879032020-11-27 Predictability limit of partially observed systems Abeliuk, Andrés Huang, Zhishen Ferrara, Emilio Lerman, Kristina Sci Rep Article 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. Nature Publishing Group UK 2020-11-24 /pmc/articles/PMC7687903/ /pubmed/33235260 http://dx.doi.org/10.1038/s41598-020-77091-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Abeliuk, Andrés Huang, Zhishen Ferrara, Emilio Lerman, Kristina Predictability limit of partially observed systems |
title | 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_short | Predictability limit of partially observed systems |
title_sort | predictability limit of partially observed systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687903/ https://www.ncbi.nlm.nih.gov/pubmed/33235260 http://dx.doi.org/10.1038/s41598-020-77091-1 |
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