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http://hdl.handle.net/20.500.12358/24109
TitleComparative Approach of Box-Jenkins Models and Artificial Neural Network Models on Births per Month in Gaza Strip Using R
Untitled
Abstract

Comparative studies of different forecasting techniques can facilitate the selection of the best time series model for forecasting future expectations. In the present study, we address this problem by comparing the forecasting performance of the SARIMA model and four typical artificial neural networks, namely, MLP, ERNN, JRNN, and RBFNN in short-term forecasting for Births in Gaza Strip. Analyses of Neural network models are done by the package (RSNNS) that is implemented in R program. We conclude that forecasting with ANNs is accurate and more efficient than the SARIMA. In addition, the most accurate ANNs model among the four examined Neural Networks is RBFNN.

Authors
Baker, Suhaila A.
Iqelan, Bisher M.
TypeJournal Article
Date2017
LanguageEnglish
Subjects
rsnns package
arima
mlp
elman neural network
jordan neural network
rbf neural network
Published inIUG Journal of Natural Studies: (Special Issue) The Sixth International Conference on Science & Development � 14-15 march 2017
SeriesVolume: 25, Number: 2
Publisherالجامعة الإسلامية - غزة
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  • Staff Publications- Faculty of Science [1030]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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