Freitas, Paulo S. A.Rodrigues, António J. L.2021-07-212021-07-212006Freitas, 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.2005.06.057http://hdl.handle.net/10400.13/3565This 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.engForecastingNeural networksModel combinationAdaptive methodsOptimal decision-making.Faculdade de Ciências Exatas e da Engenhariajournal article10.1016/j.ejor.2005.06.057