Neural network-based uncertainty quantification: a survey of methodologies and applications

Kabir, Hussain Mohammed Dipu, Khosravi, Abbas, Hosen, Mohammad and Nahavandi, Saeid 2018, Neural network-based uncertainty quantification: a survey of methodologies and applications, IEEE access, vol. 6, pp. 36218-36234, doi: 10.1109/ACCESS.2018.2836917.

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Title Neural network-based uncertainty quantification: a survey of methodologies and applications
Author(s) Kabir, Hussain Mohammed DipuORCID iD for Kabir, Hussain Mohammed Dipu
Khosravi, AbbasORCID iD for Khosravi, Abbas
Hosen, MohammadORCID iD for Hosen, Mohammad
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Journal name IEEE access
Volume number 6
Start page 36218
End page 36234
Total pages 17
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018
ISSN 2169-3536
Keyword(s) prediction interval
uncertainty quantification
heteroscedastic uncertainty
neural network
time series data
science & technology
computer science, information systems
engineering, electrical & electronic
computer science
Summary Uncertainty quantification plays a critical role in the process of decision making and optimization in many fields of science and engineering. The field has gained an overwhelming attention among researchers in recent years resulting in an arsenal of different methods. Probabilistic forecasting and in particular prediction intervals (PIs) are one of the techniques most widely used in the literature for uncertainty quantification. Researchers have reported studies of uncertainty quantification in critical applications such as medical diagnostics, bioinformatics, renewable energies, and power grids. The purpose of this survey paper is to comprehensively study neural network-based methods for construction of prediction intervals. It will cover how PIs are constructed, optimized, and applied for decision-making in presence of uncertainties. Also, different criteria for unbiased PI evaluation are investigated. The paper also provides some guidelines for further research in the field of neural network-based uncertainty quantification.
Language eng
DOI 10.1109/ACCESS.2018.2836917
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, IEEE
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