Publicação
Applying the artificial neural network methodology for forecasting the tourism time series
| Resumo: | The objective of the present research is to develop a model and apply it to sensitivity studies in order to predict demand. Provides a deeper understanding of the tourism sector in Northern Portugal and contributes to already existing econometric studies by using the Artificial Neural Networks methodology. In this methodology we use a nonlinear model inspired by the architecture of the human brain as well as the way it processes information. Artificial Neural Networks can be defined as structures comprised of compactly interconnected adaptive simple processing elements (called artificial neurons or nodes) that are capable of performing massively parallel computations for data processing and knowledge representation. Neural Networks are able to learn from the data and experience, identify the pattern or trend, and make generalization to the future. |
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| Autores principais: | Fernandes, Paula Odete |
| Outros Autores: | Teixeira, João Paulo |
| Assunto: | Artificial neural networks Training logistic activation function Backpropagation Feed-forward Time series forecast |
| Ano: | 2008 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | The objective of the present research is to develop a model and apply it to sensitivity studies in order to predict demand. Provides a deeper understanding of the tourism sector in Northern Portugal and contributes to already existing econometric studies by using the Artificial Neural Networks methodology. In this methodology we use a nonlinear model inspired by the architecture of the human brain as well as the way it processes information. Artificial Neural Networks can be defined as structures comprised of compactly interconnected adaptive simple processing elements (called artificial neurons or nodes) that are capable of performing massively parallel computations for data processing and knowledge representation. Neural Networks are able to learn from the data and experience, identify the pattern or trend, and make generalization to the future. |
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