Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 917314

Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting


Folgieri, Raffaella; Baldigara, Tea; Mamula, Maja
Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting // ToSEE - Tourism in Southern and Eastern Europe 2017
Opatija, 2017. str. 169-182 doi:10.20867/tosee.04.10 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 917314 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting

Autori
Folgieri, Raffaella ; Baldigara, Tea ; Mamula, Maja

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
ToSEE - Tourism in Southern and Eastern Europe 2017 / - Opatija, 2017, 169-182

Skup
Tourism in Southern and Eastern Europe 2017

Mjesto i datum
Opatija, Hrvatska, 4-6 May 2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Artificial Neural Networks, Econometrics, Forecasting, Artificial Intelligence, Machine Learning, Prediction

Sažetak
Purpose – Tourism is a growing sector, playing an important role in many economies, always looking for methods to provide tourism demand forecasting and new creative ideas to develop local tourist offer. Early prediction on the tourist inflow represents a challenge helping local economy to optimize and develop tourist income. Forecasting models for international tourism demand have usually mainly been focused on factors affecting the tourist inflow, following an approach that is time consuming and expensive in developing econometric models. Design – We modelled a backpropagation Artificial Neural Network (a Machine Learning Method for Decision Support and Pattern Discovery) to forecast tourists arrivals in Croatia and compared the results with those obtained with the linear regression methods. Methodology –The accuracy of the neural network has been measured by the Mean Squared Error (MSE) and compared to MSE and R2 obtained with the linear regression. Approach – Our approach consists in combining ideas from Tourism Economics and Information Technology, in particular Machine Learning, with the aim of presenting creative applications of algorithms, such as the Artificial Neural Networks (ANN), to the tourism sector. Findings – The results showed that using the neural network model to predict tourists arrivals outperforms linear regression techniques. Originality of the research –The idea to use ANN as a Decision Making tool to improve tourist services in a proactive way or in case of unexpected events is innovative. Moreover, in our final consideration, we will also present other possible creative improvements of the method

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija



POVEZANOST RADA


Projekti:
ZP UNIRI 5/16

Ustanove:
Fakultet za menadžment u turizmu i ugostiteljstvu, Opatija

Profili:

Avatar Url Maja Gregorić (autor)

Avatar Url Tea Baldigara (autor)

Citiraj ovu publikaciju

Folgieri, Raffaella; Baldigara, Tea; Mamula, Maja
Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting // ToSEE - Tourism in Southern and Eastern Europe 2017
Opatija, 2017. str. 169-182 doi:10.20867/tosee.04.10 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Folgieri, R., Baldigara, T. & Mamula, M. (2017) Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting. U: ToSEE - Tourism in Southern and Eastern Europe 2017 doi:10.20867/tosee.04.10.
@article{article, year = {2017}, pages = {169-182}, DOI = {10.20867/tosee.04.10}, keywords = {Artificial Neural Networks, Econometrics, Forecasting, Artificial Intelligence, Machine Learning, Prediction}, doi = {10.20867/tosee.04.10}, title = {Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting}, keyword = {Artificial Neural Networks, Econometrics, Forecasting, Artificial Intelligence, Machine Learning, Prediction}, publisherplace = {Opatija, Hrvatska} }
@article{article, year = {2017}, pages = {169-182}, DOI = {10.20867/tosee.04.10}, keywords = {Artificial Neural Networks, Econometrics, Forecasting, Artificial Intelligence, Machine Learning, Prediction}, doi = {10.20867/tosee.04.10}, title = {Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting}, keyword = {Artificial Neural Networks, Econometrics, Forecasting, Artificial Intelligence, Machine Learning, Prediction}, publisherplace = {Opatija, Hrvatska} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Conference Proceedings Citation Index - Science (CPCI-S)


Citati





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font