Neural computing: theory and practice
Neural computing: theory and practice
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Artificial Neural Networks: Approximation and Learning Theory
Artificial Neural Networks: Approximation and Learning Theory
An Extended Elman Net for Modeling Time Series
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Time series prediction evolving Voronoi regions
Applied Intelligence
A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series
Neural Processing Letters
A hybrid neural model in long-term electrical load forecasting
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information. The interest in this work is the development of nonlinear neural models for the purpose of building multi-step time series prediction schemes. In that context, the most popular neural models are based on the traditional feedforward neural networks. However, this kind of model may present some disadvantages when a long-term prediction problem is formulated because they are trained to predict only the next sampling time. In this paper, a neural model based on a partially recurrent neural network is proposed as a better alternative. For the recurrent 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. In order to validate the performance of the recurrent neural model to predict the dynamic behaviour of the series in the future, three different data time series have been used as study cases. An artificial data time series, the logistic map, and two real time series, sunspots and laser data. Models based on feedforward neural networks have also been used and compared against the proposed model. The results suggest than the recurrent model can help in improving the prediction accuracy.