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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 22, NO. 3, MARCH 2011 337 Lower Upper Bound Estimation Method for
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 22, NO. 3, MARCH 2011 337
Lower Upper Bound Estimation Method for
Construction of Neural Network-Based Prediction
Intervals
Abbas Khosravi, Member, IEEE, Saeid Nahavandi, Senior Member, IEEE,
Doug Creighton, and Amir F. Atiya, Senior Member, IEEE
Abstract--Prediction intervals (PIs) have been proposed in
the literature to provide more information by quantifying the
level of uncertainty associated to the point forecasts. Traditional
methods for construction of neural network (NN) based PIs
suffer from restrictive assumptions about data distribution and
massive computational loads. In this paper, we propose a new,
fast, yet reliable method for the construction of PIs for NN
predictions. The proposed lower upper bound estimation (LUBE)
method constructs an NN with two outputs for estimating the
prediction interval bounds. NN training is achieved through the
minimization of a proposed PI-based objective function, which
covers both interval width and coverage probability. The method
does not require any information about the upper and lower
bounds of PIs for training the NN. The simulated annealing

  

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

 

Collections: Computer Technologies and Information Sciences