Comparative approach of Box-Jenkins models and artificial neural network models on births per month in Gaza Strip using R.

Other Title(s)

دراسة مقارنة لنماذج بوكس-جنكنز و نماذج الشبكة العصبية الاصطناعية على مواليد قطاع غزة باستخدام برنامج R

Joint Authors

Bakir, Suhaylah A.
Iqilan, Bishir M.

Source

مجلة الجامعة الإسلامية للدراسات الطبيعية

Publisher

The Islamic University-Gaza Deanship of Research and Graduate Affairs

Publication Date

2017-03-31

Country of Publication

Palestine (Gaza Strip)

No. of Pages

7

Main Subjects

Mathematics

Topics

• Gaza Strip
• Infants
• Box-Jenkins forecasting
• Neural networks(Computer science)

English 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.

Data Type

Conference Papers

Record ID

BIM-778519

• APA
• MLA
• AMA