FORECASTING DYNAMIC TOURISM DEMAND USING ARTIFICIAL NEURAL NETWORKS

Andreeski, Cvetko and Petrevska, Biljana (2021) FORECASTING DYNAMIC TOURISM DEMAND USING ARTIFICIAL NEURAL NETWORKS. Journal of Electrical Engineering and Information Technologies, 6 (2). pp. 79-89. ISSN 2545–4269

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Abstract

Planning tourism development means preparing the destination for coping with uncertainties as tourism is sensitive to many changes. This study tested two types of artificial neural networks in modeling international tourist arrivals recorded in Ohrid (North Macedonia) during 2010–2019. It argues that the MultiLayer Perceptron (MLP) network is more accurate than the Nonlinear AutoRegressive eXogenous (NARX) model when forecasting tourism demand. The research reveals that the bigger the number of neurons may not necessarily lead to further perfor-mance improvement of the model. The MLP network for its better performance in modeling series with unexpected challenges is highly recommended for forecasting dynamic tourism demand

Item Type:	Article
Subjects:	Scientific Fields (Frascati) > Engineering and Technology > Electrical engineering, electronic engineering,information engineering
Divisions:	Faculty of Tourism and Hospitality
Depositing User:	Prof. d-r Cvetko Andreeski
Date Deposited:	04 Apr 2023 10:00
Last Modified:	20 Apr 2023 06:28
URI:	https://eprints.uklo.edu.mk/id/eprint/7907

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