Articolo
| DOI |
10.7336/academicus.2014.10.14 |
| URL |
https://academicus.edu.al/?subpage=volumes&nr=10 |
| Per questo DOI è attiva la Multiple Resolution: |
| MR URL |
https://academicus.edu.al |
| MR URL |
https://academicus.edu.al/nr10/Academicus-MMXIV-10-202-211.html |
| MR URL |
https://academicus.edu.al/nr10/Academicus-MMXIV-10-202-211.pdf |
| MR URL |
mailto:info@academicus.edu.al |
| MR URL |
https://academicus.edu.al/images/front_end/academicus.jpg |
| MR URL |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| DATI DI ACCESSO: |
|
OA - Accesso aperto |
| Licenza OA |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Titolo completo
Inglese
(eng)
|
Academicus International Scientific Journal |
| Editore (01) |
Academicus International Scientific Journal |
| Paese di pubblicazione |
Albania
(AL)
|
|
ISSN
|
20793715 |
| Formato del prodotto |
Rivista Stampata
(JB)
|
|
ISSN
|
23091088 |
| Formato del prodotto |
Rivista Online
(JD)
|
| Numero del volume |
10 |
| Data del fascicolo (YYYY/MM) |
2014 / 07 |
Titolo
Inglese
(eng)
|
Artificial neural networks in forecasting tourists’ flow, an intelligent technique to help the economic development of tourism
in Albania.
|
|
Di (autore)
(A01)
|
Dezdemona Gjylapi |
| Affiliazione |
University “Pavaresia” Vlore, Albania, Doctoral Candidate |
|
Di (autore)
(A01)
|
Veronika Durmishi |
| Affiliazione |
"University of Vlore ""Ismail Qemali"", Albania", Dr. |
| Numero di Pagine |
10 |
| Prima Pagina |
202 |
| Ultima Pagina |
211 |
| Lingua del testo |
Inglese
(eng)
|
| Data di publicazione (YYYY/MM) |
2014 / 07 |
| Copyright |
2014, Academicus |
Abstract
Descrizione principale
(01)
|
Tourism plays an important role in many economies and contributes greatly to the Gross Domestic Product. In the past eight
years, the number of tourist arrivals in Albania has increased rapidly, which resulted in increasing the number of tourist
nights and revenue from tourism. Tourism also provides new sources of income for the country, without having that local citizen
to pay more taxes. This can be achieved by income from parking, tourist taxes, leased apartments, sales information, etc.
Early prediction on the tourist inflow mainly focuses on econometric models that have as a main feature the tourism demand
being predicted by analysing factors that affect the tourists’ inflow. This approach results in being difficult, time-consuming
and also expensive to determine econometric models. Traditional time series methods, such as exponential smoothing method,
grey prediction method, linear regression method, ARIMA method etc., are more appropriate for the prediction of the tourist
inflow. However, since they don’t apply a learning process on sample data, it is difficult for them to realize complicated
and non-linear prediction on tourist inflow. The aim of this paper is to present the neural network usage in the tourists’
number forecasting and to determine the trends of the future tourist inflow, thus helping tourism management agencies in making
scientific based financial decisions.
|
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