The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals.
History
Event
International Joint Conference on Neural Network (2011 : San Jose, Calif.)
Pagination
3072 - 3078
Publisher
IEEE
Location
San Jose, Calif.
Place of publication
Piscataway, N. J.
Start date
2011-07-31
End date
2011-08-05
ISSN
2161-4393
ISBN-13
9781424496358
ISBN-10
1424496357
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2011, IEEE
Title of proceedings
IJCNN 2011 : Proceedings of the International Joint Conference on Neural Networks