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. Attached Files Name Description MIMEType Size Downloads Title Neural network-based uncertainty quantification: a survey of methodologies and applications Author(s) Kabir, Hussain Mohammed Dipu orcid.org/0000-0002-3395-1772 Khosravi, Abbas orcid.org/0000-0001-6927-0744 Hosen, Mohammad orcid.org/0000-0001-8282-3198 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 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 forecast time series data regression probability science & technology technology computer science, information systems engineering, electrical & electronic telecommunications computer science engineering 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30111072 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment Institute for Intelligent Systems Research and Innovation (IISRI) School of Engineering Open Access Collection GTP Research Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 12 Jul 2018, 12:24:04 EST Fri, 13 Jul 2018, 05:03:36 EST Fri, 13 Jul 2018, 08:13:07 EST Sat, 18 Aug 2018, 02:30:22 EST Mon, 26 Nov 2018, 11:43:11 EST Mon, 26 Nov 2018, 11:44:26 EST Mon, 26 Nov 2018, 11:45:18 EST Thu, 07 Feb 2019, 05:02:35 EST Wed, 13 Feb 2019, 09:29:22 EST Thu, 14 Nov 2019, 03:38:42 EST Filtered Full Citation counts:   Cited 5 times in TR Web of Science Cited 12 times in Scopus Search Google Scholar Access Statistics: 154 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 12 Jul 2018, 12:24:04 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.