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Model combination in neural-based forecasting
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Orientador(es)
Resumo(s)
This paper discusses different ways of combining neural predictive models or neural-based forecasts. The proposed
approaches consider Gaussian radial basis function networks, which can be efficiently identified and estimated through
recursive/adaptive methods. The usual framework for linearly combining estimates from different models is extended,
to cope with the case where the forecasting errors from those models are correlated. A prefiltering methodology is pro posed, addressing the problems raised by heavily nonstationary time series. Moreover, the paper discusses two
approaches for decision-making from forecasting models: either inferring decisions from combined predictive estimates,
or combining prescriptive solutions derived from different forecasting models.
Descrição
Palavras-chave
Forecasting Neural networks Model combination Adaptive methods Optimal decision-making . Faculdade de Ciências Exatas e da Engenharia
Contexto Educativo
Citação
Freitas, P. S., & Rodrigues, A. J. (2006). Model combination in neural-based forecasting. European Journal of Operational Research, 173(3), 801-814. DOI: 10.1016/j.ejor.2005.06.057
Editora
Elsevier