REDES NEURAIS E REGRESSÃO DINÂMICA: UM MODELO HÍBRIDO PARA PREVISÃO DE CURTO PRAZO DA DEMANDA DE GASOLINA AUTOMOTIVA NO BRASIL / NEURAL NETWORK AND DYNAMIC REGRESSION: A HYBRID MODEL TO FORECAST THE SHORT TERM DEMAND OF PETROL IN BRAZIL

AUTOR(ES)

ALEXANDRE ZANINI

DATA DE PUBLICAÇÃO

2000

RESUMO

In this dissertation a short term model to forecast automotive gasoline demand in Brazil is proposed. From the methodology 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 sits 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 results is highly desirable in modeling time series and, particularly, to the short term forecast of automotive gasoline, object of this dissertation.

ASSUNTO(S)

petrol redes neurais modelos estatisticos demand forecast neural networks previsao de demanda gasolina automotiva statistical models

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