Título: Um Modelo Híbrido para Previsão de Curto Prazo da Demanda de Gasolina Automotiva no Brasil
Autores: Zanini, Alexandre; Souza, Reinaldo Castro; Pedreira, Carlos Eduardo
Resumo: In this paper a short term model to forecast automotive gasoline demand in Brazil is proposed. From the metodology point of view, data is analyzed and a model using a bottom-up strategy is developed. In other words, a simple model is improved step by step until a proper model that fits well the reality is found. Departuring from a univariate model it ends up in a neural network formulation, passing through dynamic regression models. The models obtained in this scheme are compared according to some criterion, mainly forecast accuracy. We conclude, that the efficiency of putting together classical statistics models (such as Box & Jenkins and dynamic regression) and neural networks improve the forecasting results. This result is highly desirable in modeling time series and, particularly, to the short term forecast of automotive gasoline, object of this paper.
Código DOI: 10.21528/CBRN2001-086
Artigo em pdf: 5cbrn_086.pdf
Arquivo BibTex: 5cbrn_086.bib