Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2012.6252452
Title: Construction of neural network-based prediction intervals using particle swarm optimization
Authors: Quan, H.
Srinivasan, D. 
Khosravi, A.
Issue Date: 2012
Source: Quan, H.,Srinivasan, D.,Khosravi, A. (2012). Construction of neural network-based prediction intervals using particle swarm optimization. Proceedings of the International Joint Conference on Neural Networks : -. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2012.6252452
Abstract: Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner. © 2012 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/69709
ISBN: 9781467314909
DOI: 10.1109/IJCNN.2012.6252452
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