000			01857naa a2200205uu 4500
001			7081011185149
003			OSt
005			20190211181906.0
008			170810s2016 xx ||||gr |0|| 0 eng d
100	1		_aCONSTANTINO, H. A. _955503
245	1	0	_aTourism demand modelling and forecasting with artificial neural network models : _bThe Mozambique case study
260			_aBarcelos : _bIPCA, _cjul./dez. 2016
520	3		_aThis study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand
700	1		_aFERNANDES, P. O. _955504
700	1		_aTEIXEIRA, J. P. _955505
773	0	8	_tTékhne-Revista de Estudos Politécnicos = Polytechnical Studies Review _g14, 2, p. 113-124 _dBarcelos : IPCA, jul./dez. 2016 _xISSN 16459911 _w
856	4	2	_uhttp://ac.els-cdn.com/S164599111630010X/1-s2.0-S164599111630010X-main.pdf?_tid=c5b44968-7dd6-11e7-8bbb-00000aacb360&acdnat=1502374885_727858073d553a098ca2c7dc4f6bc130 _yAcesso
942			_cS
998			_a20170810 _b1118^b _cLarissa
999			_aConvertido do Formato PHL _bPHL2MARC21 1.1 _c51848 _d51848
041			_aeng