Modeling and Forecasting of Tourist Arrivals in Crete Using Statistical Models and Models of Computational Intelligence
| Autor: | Sofoklis Xristoforidis, Stefanos Goumas, Stavros Kontakos, Aikaterini G. Mathheaki |
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| Rok vydání: | 2021 |
| Předmět: |
Information Systems and Management
Computer Networks and Communications Computer science business.industry Statistical model Computational intelligence Machine learning computer.software_genre Computer Science Applications Management Information Systems Computational Theory and Mathematics Artificial intelligence business computer Tourism Information Systems |
| Zdroj: | International Journal of Operations Research and Information Systems. 12:58-72 |
| ISSN: | 1947-9336 1947-9328 |
| Popis: | In the past few decades, tourism has clearly become one of the most prominent economic trends for many countries. For many destinations, this trend will continue to rise, and tourism will become the most dynamic and fastest growing sector of the economy. Thus, the reliable and accurate forecasting of tourism demand is necessary in making decisions for effective and efficient planning of tourism policy. The objective of this paper is the modeling and forecasting the international tourist arrivals to four prefectures of Crete in the year 2012, based on the actual tourist arrivals data over the period 1993 – 2011, using one-step-ahead forecast. In particular, this paper presented a comparative study of time series forecasts of international travel demand for the four prefectures of Crete using a variety of statistical quantitative forecasting models along with neural networks and fuzzy models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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