RT Conference Proceedings
T1 Neural recurrent model for multi-step time series prediction
A1 Galván, Inés M.
A1 Nogal-Quintana, E.
A1 Alonso Weber, Juan Manuel
AB This 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.
PB IOS Press
SN 9789051994759
YR 1999
FD 1999
LK https://hdl.handle.net/10016/4521
UL https://hdl.handle.net/10016/4521
LA eng
NO Proceeding of: aInternational Conference on Computational Intelligence for Modelling, Control and Automation - CIMCA'99, Vienna - Austria, 17-19 February 1999
DS e-Archivo
RD 19 may. 2024