Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Neural Networks in Business Forecasting
Neural Networks in Business Forecasting
A new computational method of input selection for stock market forecasting with neural networks
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
A delay damage model selection algorithm for NARX neural networks
IEEE Transactions on Signal Processing
Computational capabilities of recurrent NARX neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
Comparison of artificial neural networks using prediction benchmarking
ACMOS'11 Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation
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The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural networks (RNN) models are not only important for the forecasting of time series but also generally for the control of the dynamical system. A RNN with a sufficiently large number of neurons is a nonlinear autoregressive and moving average (NARMA) model, with "moving average" referring to the inputs. The prediction can be assimilated to identification of dynamic process. An architectural approach of RNN with embedded memory, "Nonlinear Autoregressive model process with eXogenous input" (NARX), showing promising qualities for dynamic system applications, is analyzed in this paper. The performances of the NARX model are verified for several types of chaotic or fractal time series applied as input for neural network, in relation with the number of neurons, the training algorithms and the dimensions of his embedded memory. In addition, this work has attempted to identify a way to use the classic statistical methodologies (R/S Rescaled Range analysis and Hurst exponent) to obtain new methods of improving the process efficiency of the prediction chaotic time series with NARX.