Dataset Information

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.

SUBMITTER: Abeliuk A

PROVIDER: S-EPMC7687903 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Predictability limit of partially observed systems.

Abeliuk Andrés A Huang Zhishen Z Ferrara Emilio E Lerman Kristina K

Scientific reports 20201124 1

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 syste ...[more]

PMID: 33235260

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Predictability limit of partially observed systems.

Publications

Predictability limit of partially observed systems.

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