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 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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Abeliuk A,Huang Z,Ferrara E,Lerman K

doi

10.1038/s41598-020-77091-1

subject

Has Abstract

pub_date

2020-11-24 00:00:00

pages

20427

issue

1

issn

2045-2322

pii

10.1038/s41598-020-77091-1

journal_volume

10

pub_type

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