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Aggregation of Pi-based forecast to enhance prediction accuracy

Hosen,MA, Khosravi,A, Nahavandi,S and Creighton,D 2014, Aggregation of Pi-based forecast to enhance prediction accuracy, in IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 778-784, doi: 10.1109/IJCNN.2014.6889464.

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Title Aggregation of Pi-based forecast to enhance prediction accuracy
Author(s) Hosen,MAORCID iD for Hosen,MA orcid.org/0000-0001-8282-3198
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Nahavandi,S
Creighton,D
Conference name International Joint Conference on Neural Networks (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2014
Conference series International Joint Conference on Neural Networks
Start page 778
End page 784
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Summary In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.
ISBN 9781479914845
Language eng
DOI 10.1109/IJCNN.2014.6889464
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070733

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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