Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Moro, S. & Rita, P. (2016). Forecasting tomorrow’s tourist. Worldwide Hospitality and Tourism Themes. 8 (6), 643-653
Exportar Referência (IEEE) 
S. M. Moro and P. M. Rita,  "Forecasting tomorrow’s tourist", in Worldwide Hospitality and Tourism Themes, vol. 8, no. 6, pp. 643-653, 2016
Exportar BibTeX 
@article{moro2016_1775859553785,
	author = "Moro, S. and Rita, P.",
	title = "Forecasting tomorrow’s tourist",
	journal = "Worldwide Hospitality and Tourism Themes",
	year = "2016",
	volume = "8",
	number = "6",
	doi = "10.1108/WHATT-09-2016-0046",
	pages = "643-653",
	url = "http://www.emeraldinsight.com/doi/abs/10.1108/WHATT-09-2016-0046"
}
Exportar RIS 
TY  - JOUR
TI  - Forecasting tomorrow’s tourist
T2  - Worldwide Hospitality and Tourism Themes
VL  - 8
IS  - 6
AU  - Moro, S.
AU  - Rita, P.
PY  - 2016
SP  - 643-653
SN  - 1755-4217
DO  - 10.1108/WHATT-09-2016-0046
UR  - http://www.emeraldinsight.com/doi/abs/10.1108/WHATT-09-2016-0046
AB  - Purpose: This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach: For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings: The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value:
The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.
ER  -