Subject: Other social sciences
Year: 2021
Type: Conference or Workshop Item
Type: PeerReviewed
Title: Forecasting dynamic tourism demand using Artificial Neural Networks
Author: Andreeski, Cvetko
Author: Petrevska, Biljana
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 performance improvement of the model. The MLP network for its better performance in modelling series with unexpected challenges is highly recommended for forecasting dynamic tourism demand.
Publisher:
Relation: https://eprints.ugd.edu.mk/29215/
Identifier: oai:eprints.ugd.edu.mk:29215
Identifier: https://eprints.ugd.edu.mk/29215/2/Forecasting%20dynamic%20tourism%20demand.pdfIdentifier: Andreeski, Cvetko and Petrevska, Biljana (2021) Forecasting dynamic tourism demand using Artificial Neural Networks. In: XV international conference ETAI 2021, 23-24.09.2021, online via Zoom platform.
