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Artificial neural network and regression models for predicting Fiji population

Qiokata, Viliame and Khan, Mohammad G.M. (2015) Artificial neural network and regression models for predicting Fiji population. [Conference Proceedings]

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Abstract

The paper compares Artificial Neural Network (ANN) model against traditional models in the modeling of population and external migration for Fiji population components during the years from 1986 to 2012. The performance of the various models used are based on the values of the various error functions such as the R-squared (R2), Root Square Mean Error (RSME), Mean Absolute Error (MAE), Standard Error of Regression (SER), Sum Squared Residual (SSR), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Across yearly time series, ANN performed better than those traditional models, when comparing the various error functions used.

Item Type: Conference Proceedings
Additional Information: DOI: 10.1109/APWCCSE.2014.7053850
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Divisions: College of Foundation Studies
Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Fulori Nainoca
Date Deposited: 16 Mar 2015 12:34
Last Modified: 15 Mar 2017 15:48
URI: http://repository.usp.ac.fj/id/eprint/8127
UNSPECIFIED

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