Predictability limit of partially observed systems
Author
dc.contributor.author
Abeliuk Kimelman, Andrés
Author
dc.contributor.author
Huang, Zhishen
Author
dc.contributor.author
Ferrara, Emilio
Author
dc.contributor.author
Lerman, Kristina
Admission date
dc.date.accessioned
2021-05-27T23:12:15Z
Available date
dc.date.available
2021-05-27T23:12:15Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Scientifc Reports (2020) 10:20427
es_ES
Identifier
dc.identifier.other
10.1038/s41598-020-77091-1
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/179857
Abstract
dc.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.
es_ES
Patrocinador
dc.description.sponsorship
Office of the Director of National Intelligence (ODNI)
FA8750-16-C-0112
Intelligence Advanced Research Projects Activity (IARPA) via the Air Force Research Laboratory (AFRL)
FA8750-16-C-0112
United States Department of Defense
Defense Advanced Research Projects Agency (DARPA)
W911NF17-C-0094