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Primjena Umjetnih Neuronskih Mreža U Modeliranju Domaće Turističke Potražnje

Author

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  • Tea Baldigara

    (Fakultet za menadžment u turizmu i ugostiteljstvu Opatija, Sveučilište u Rijeci)

Abstract

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.

Suggested Citation

  • Tea Baldigara, 2022. "Primjena Umjetnih Neuronskih Mreža U Modeliranju Domaće Turističke Potražnje," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), vol. 73(3), pages 349-370.
    • Handle: RePEc:hde:epregl:v:73:y:2022:i:3:p:349-370
    DOI: 10.32910/ep.73.3.1
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    Keywords

    domaća turistička potražnja; modeliranje; modeli umjetnih neuronskih mreža; model višeslojnog perceptrona;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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