Authors :
Safaa J. Alwan; Hassan M. Ibrahim; Ali N. Yousif
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/pejfnna8
Scribd :
http://tinyurl.com/5xyk9wk8
DOI :
https://doi.org/10.5281/zenodo.10516679
Abstract :
Several Artificial intelligence techniques can
help predict future values of time and provide guidance
on social and economic development plans. The goal of
this study was to analyze the Iraqi economy using neural
networks. It was able to predict the country's gross
domestic product from 2003 to 2020. Thirty-six networks
of different types (feed-forward backpropagation,
NARX, Layer Recurrent Network (LRN)) were built.
The recommended model was chosen according to the
RMSE criterion. The Iraqi GDP prediction was made
using an artificial neural network that utilized the
TRAINBR training and transit functions. It performed
well and earned the lowest error value.
Keywords :
Gross Domestic Product, Time Series, Artificial Neural Networks.
Several Artificial intelligence techniques can
help predict future values of time and provide guidance
on social and economic development plans. The goal of
this study was to analyze the Iraqi economy using neural
networks. It was able to predict the country's gross
domestic product from 2003 to 2020. Thirty-six networks
of different types (feed-forward backpropagation,
NARX, Layer Recurrent Network (LRN)) were built.
The recommended model was chosen according to the
RMSE criterion. The Iraqi GDP prediction was made
using an artificial neural network that utilized the
TRAINBR training and transit functions. It performed
well and earned the lowest error value.
Keywords :
Gross Domestic Product, Time Series, Artificial Neural Networks.
