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Constructing optimal prediction intervals by using neural networks and bootstrap method
journal contribution
posted on 2015-08-08, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, D Srinivasan, Rihanna KhosraviThis brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.
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Journal
IEEE Transactions on neural networks and learning systemsVolume
26Issue
8Pagination
1810 - 1815Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
eISSN
2162-2388Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, IEEEUsage metrics
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