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Pregled bibliografske jedinice broj: 1069780

ARTIFICIAL NEURAL NETWORKS IN MODELLING SEASONAL TOURISM DEMAND – CASE STUDY OF CROATIA


Gregorić, Maja; Baldigara, Tea
ARTIFICIAL NEURAL NETWORKS IN MODELLING SEASONAL TOURISM DEMAND – CASE STUDY OF CROATIA // Zbornik Veleučilišta u Rijeci / Journal of the Polytechnic of Rijeka, 8 (2020), 1; 19-39 doi:10.31784/zvr.8.1.2 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1069780 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
ARTIFICIAL NEURAL NETWORKS IN MODELLING SEASONAL TOURISM DEMAND – CASE STUDY OF CROATIA

Autori
Gregorić, Maja ; Baldigara, Tea

Izvornik
Zbornik Veleučilišta u Rijeci / Journal of the Polytechnic of Rijeka (1848-1299) 8 (2020), 1; 19-39

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Croatian tourism ; seasonality ; Hegy seasonal unit root test ; artificial neural network model ; MAPE

Sažetak
The purpose of this paper is to design an artificial neural network in the attempt to define the data generating process of the number of German tourist arrivals in Croatia considering the strong seasonal character of empirical data. The presence of seasonal unit roots in tourism demand determinants is analysed using the approach developed by Hylleberg, Engle, Granger and Yoo – Hegy test. The study is based on seasonality analysis and Artificial Neural Networks approach in building a model which intend to describe the behaviour of the German tourist flows to Croatia. Different neural network architectures were trained and tested, and after the modelling phase, the forecasting accuracy and model performances were analysed. Model performance and forecasting accuracy evaluation was tested using the mean absolute percentage error. Based on the augmented HEGY test procedure it can be concluded the German tourist arrivals to the Republic of Croatia have nonstationary behaviour associated with the zero frequency and seasonal frequency. Taking this into consideration, in the analysis of the phenomenon it is necessary to consider its seasonal character. Given the importance of the tourism for Croatian economic development, the research results could be useful, for both, researchers and practitioners, in the process of planning and routing the future Croatian hotel industry development and improvement of business performances.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija



POVEZANOST RADA


Projekti:
ZP UNIRI 4/17

Ustanove:
Fakultet za menadžment u turizmu i ugostiteljstvu, Opatija

Profili:

Avatar Url Maja Gregorić (autor)

Avatar Url Tea Baldigara (autor)

Citiraj ovu publikaciju

Gregorić, Maja; Baldigara, Tea
ARTIFICIAL NEURAL NETWORKS IN MODELLING SEASONAL TOURISM DEMAND – CASE STUDY OF CROATIA // Zbornik Veleučilišta u Rijeci / Journal of the Polytechnic of Rijeka, 8 (2020), 1; 19-39 doi:10.31784/zvr.8.1.2 (međunarodna recenzija, članak, znanstveni)
Gregorić, M. & Baldigara, T. (2020) ARTIFICIAL NEURAL NETWORKS IN MODELLING SEASONAL TOURISM DEMAND – CASE STUDY OF CROATIA. Zbornik Veleučilišta u Rijeci / Journal of the Polytechnic of Rijeka, 8 (1), 19-39 doi:10.31784/zvr.8.1.2.
@article{article, year = {2020}, pages = {19-39}, DOI = {10.31784/zvr.8.1.2}, keywords = {Croatian tourism, seasonality, Hegy seasonal unit root test, artificial neural network model, MAPE}, journal = {Zbornik Veleu\v{c}ili\v{s}ta u Rijeci / Journal of the Polytechnic of Rijeka}, doi = {10.31784/zvr.8.1.2}, volume = {8}, number = {1}, issn = {1848-1299}, title = {ARTIFICIAL NEURAL NETWORKS IN MODELLING SEASONAL TOURISM DEMAND – CASE STUDY OF CROATIA}, keyword = {Croatian tourism, seasonality, Hegy seasonal unit root test, artificial neural network model, MAPE} }
@article{article, year = {2020}, pages = {19-39}, DOI = {10.31784/zvr.8.1.2}, keywords = {Croatian tourism, seasonality, Hegy seasonal unit root test, artificial neural network model, MAPE}, journal = {Zbornik Veleu\v{c}ili\v{s}ta u Rijeci / Journal of the Polytechnic of Rijeka}, doi = {10.31784/zvr.8.1.2}, volume = {8}, number = {1}, issn = {1848-1299}, title = {ARTIFICIAL NEURAL NETWORKS IN MODELLING SEASONAL TOURISM DEMAND – CASE STUDY OF CROATIA}, keyword = {Croatian tourism, seasonality, Hegy seasonal unit root test, artificial neural network model, MAPE} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)


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