Galván, Inés M.Nogal-Quintana, E.Alonso Weber, Juan Manuel2009-06-262009-06-261999International Conference on Computational Intelligence for Modelling, Control and Automation - CIMCA'99, Vienna - Austria, 17-19 February 1999, p. 63-689789051994759https://hdl.handle.net/10016/4521Proceeding of: aInternational Conference on Computational Intelligence for Modelling, Control and Automation - CIMCA'99, Vienna - Austria, 17-19 February 1999This paper is focused on the development of nonlinear neural models with the purpose of long-term or multistep time series prediction schemes. Multi-step prediction tries to achieve predictions several steps ahead into the future starting from information at time k. In the context of time series prediction, the most popular neural models are based on the traditional feedforward neural network. However, this kind of models may present some problems when a long-term prediction problem is formulated. In this paper, a neural model based on a partially recurrent neural network is proposed as an alternative. For the new model, a learning phase with the purpose of long-term prediction is imposed, which allows to obtain better predictions of time series in the future. The recurrent neural model has been applied to the logistic time series with the aim to predict the dynamic behaviour of the series in the future. Models based on feedforward neural networks have been also used and compared against the proposed model.application/pdfeng© IOS PressTime seriesNeural recurrent modellingconference paperInformáticaopen access6368International Conference on Computational Intelligence for Modelling, Control and Automation