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Baldigara, Tea
Ekonomski pregled, 01/2022, Volume: 73, Issue: 3Journal Article, Magazine Article, Paper
Rad istražuje učinkovitost primjene modela umjetnih neuronskih mreža u modeliranju domaće turističke potražnje u Republici Hrvatskoj, aproksimiranoj brojem dolazaka i brojem ostvarenih noćenja domaćih turista. Indeksi obujma industrijske proizvodnje, indeksi potrošačkih cijena, prosječna neto mjesečna plaća, broj zaposlenih te mjesečne sezonske dummy varijable odabrane su kao ulazne varijable. Za modeliranje empirijskih podataka korištene su mreže višeslojnog perceptrona. Prognostička moć modela evaluirana je prosječnom apsolutnom postotnom te prosječnom apsolutnom prognostičkom pogreškom, Pearsonovim koeficijentom korelacije te koeficijentom determinacije. Evaluacija dobivenih rezultata pokazala je kako su odabrani modeli višeslojnih perceptrona pouzdani za modeliranje domaće turističke potražnje, iako je istraživanje temeljeno na ograničenom, manjem broju podataka te broju ulaznih varijabli. Polazeći od rezultata, ali i ograničenja istraživanja, zaključuje se kako modeli umjetnih neuronskih mreža posjeduju značajne aplikativne potencijale u domeni modeliranja i prognoziranja vremenskih nizova broja dolazaka i noćenja domaćih turista u Republici Hrvatskoj. The paper investigates the efficiency of artificial neural networks in modelling domestic tourist demand in the Republic of Croatia, approximated by the number of arrivals and the number of realized overnight stays of domestic tourists. Industrial production volume indices, consumer price indices, average net monthly wages, the number of employees and the monthly seasonal dummy variables were selected as input variables. Two multilayer perceptrons neural networks models were used to model empirical data. The model predictive efficiency was evaluated using the mean average, mean absolute percentage, mean squared root forecast errors, as well as the coefficient of determination and the Pearsons correlation coefficient. The obtained results evaluation showed that the selected multilayer perceptrons models are reliable for modelling domestic tourism demand, although the research is based on a limited small amount of data as well as the number of input variables. Given the research results, as well as the research limitations, it can be concluded that the artificial neural networks models have significant application potentials in modelling time-series of arrivals and overnight stays of domestic tourists in the Republic of Croatia.
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