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SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS 1 A Comprehensive Review of Neural Network-based
 

Summary: SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS 1
A Comprehensive Review of Neural Network-based
Prediction Intervals and New Advances
Abbas Khosravi, Member, IEEE, Saeid Nahavandi, Senior Member, IEEE, Doug Creighton, and Amir F.
Atiya, Member, IEEE
Abstract--This study evaluates the four leading techniques pro-
posed in literature for construction of Prediction Intervals (PIs)
for Neural Network (NN) point forecasts. The delta, Bayesian,
bootstrap, and Mean­Variance Estimation (MVE) methods are
reviewed and their performance for generating high quality
PIs is compared. PI-based measures are proposed and applied
for the objective and quantitative assessment of each method's
performance. A selection of twelve synthetic and real world
case studies is used to examine each method's performance
for PI construction. The comparison is performed based on
the quality of generated PIs, the repeatability of the results,
the computational requirements, and the PIs' variability with
regard to the data uncertainty. The obtained results in this study
indicate that (i) the delta and Bayesian methods are the best
in terms of quality and repeatability, and (ii) the MVE and

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences