Statistical methods for modelling neural networks

• Autores: Timo Teräsvirta, Marcelo C. Medeiros
• Localización: International journal of engineering intelligent systems for electrical engineering and communications, ISSN 0969-1170, Vol. 9, Nº 4, 2001, págs. 227-236
• Idioma: inglés
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• Resumen

  • In this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on sta-tistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection cri-teria and tests. Mis-specification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented. Keywords: Model mis-specification, neural computing, nonlinear forecasting, nonlinear time series, smooth transition autoregression, sunspot series, threshold autoregression