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      Artificial neural networks in forecasting tourists’ flow, an intelligent technique to help the economic development of tourism in Albania.

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          Abstract

          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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          Author and article information

          Journal
          Academicus : International Scientific Journal
          Academicus Journal
          01 July 2014
          : MMXIV
          : 10
          : 202-211
          Article
          75b29bfc243a4b5680063fd33c5f1ad8
          10.7336/academicus.2014.10.14

          Distributed under the terms of the https://creativecommons.org/licenses/by-nc-nd/4.0/, which permits noncommercial use and distribution in any medium, provided the original author(s) and source are credited, and the original work is not modified.

          Product
          Categories
          Social Sciences
          H
          Economics as a science
          HB71-74

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