Session
Computer Science and Information Systems
Description
The aim of this paper is to design a neural network model trained with genetic algorithms, called 'neuro-genetic' model, and to use it in Gross Domestic Product (GDP) forecasting. GDP is an economic indicator that represents the value at market prices of all goods and services produced within a country in a given period (usually a year). Its forecasting is of particular importance for the fiscal and monetary policy makers in the preparation of timetables aiming macroeconomic stability and su stainable economic growth. We use ten factors that affect the determination of the GDP for the Albania’s GDP forecasting. The genetic algorithm is used to train the weights of different architecture MLPs. We compare the output of these neural networks (NN) and find the best NN architecture which archives the high accuracy for GDP forecasting.
Keywords:
artificial neural networks, genetic algorithm, forecasting, GDP
Session Chair
Peter Kopacek
Session Co-Chair
Selman Haxhijaha
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-437-24-0
First Page
58
Last Page
64
Location
Durres, Albania
Start Date
2-11-2013 9:45 AM
End Date
2-11-2013 10:00 AM
DOI
10.33107/ubt-ic.2013.59
Recommended Citation
Gjylapi, Dezdemona and Kasemi, Vladimir, "A Neuro-Genetic Model in GDP Forecasting: Case Study Albania" (2013). UBT International Conference. 59.
https://knowledgecenter.ubt-uni.net/conference/2013/all-events/59
Included in
A Neuro-Genetic Model in GDP Forecasting: Case Study Albania
Durres, Albania
The aim of this paper is to design a neural network model trained with genetic algorithms, called 'neuro-genetic' model, and to use it in Gross Domestic Product (GDP) forecasting. GDP is an economic indicator that represents the value at market prices of all goods and services produced within a country in a given period (usually a year). Its forecasting is of particular importance for the fiscal and monetary policy makers in the preparation of timetables aiming macroeconomic stability and su stainable economic growth. We use ten factors that affect the determination of the GDP for the Albania’s GDP forecasting. The genetic algorithm is used to train the weights of different architecture MLPs. We compare the output of these neural networks (NN) and find the best NN architecture which archives the high accuracy for GDP forecasting.