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  • Modeling seek tourist in Portugal: linear regression "versus" Artificial Neural Networks.

    Foreign Title : Modelaçao da procuera turística em Portugal: regressao linear "versus" Redes Neuronais Artificiais.

    Author(s) : Machado, T. N. M.Teixeira, J. P. R.Fernandes, P. O.

    Journal article : Revista Turismo & Desenvolvimento 2010 No.13/14, Vol. 1 pp.435-445

    Abstract : The modulation and forecast of economic time series related with tourism showed an increase interesting, in last years, due to the relevance of the tourism sector for the Portuguese economy. Hence, the central aim of the present paper consists in the comparative study between the linear regression based model and the Artificial Neural Network (ANN) based model. The inclusion of these two different models has the purpose of understand their potentiality to deal with the peculiar characteristics of the tourism time series such as seasonality and trend. The monthly series that measure the tourism demand "Monthly Guest Nights in Hotels" between January 1990 and December 2008 was used. The developed models achieved a high level of statistical quality of adjustment, and therefore they were used for forecast purposes. A comparison between forecast values and original data for the years of 2007 and 2008 were made. The error, measured by the average of the percentage absolute error (EPAM), for the forecast in that period was 4.2% and 4.1% for the linear regression model and ANN model respectively.

    ISSN : 1645-9261

    URL : http://dialnet.unirioja.es/servlet/ar...

    Record Number : 20113400606

    Publisher : Universidade de Aveiro

    Location of publication : Aveiro

    Country of publication : Portugal

    Language of text : Portuguese

    Language of summary : English

    Indexing terms for this abstract:

    Descriptor(s) : demand, mathematical models, neural networks, regression analysis, time series, tourism

    Geographical Location(s) : Portugal

    Broader term(s) : Community of Portuguese Language Countries, Developed Countries, European Union Countries, Mediterranean Region, OECD Countries, Southern Europe, Europe