A New Methodology for Neural Network Training Ensures Error Reduction in Time Series Forecasting
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Fecha
2017-06-30
Autores
Sánchez-Sánchez, Paola A.
García-González, José Rafael
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Science publications
Resumen
Artificial Neural Networks (ANN) consists of some components,
such as architecture and learning algorithm. These components have a
significant effect on the performance of the ANN, but finding good
parameters is a difficult task to achieve. An important requirement for this
task is to ensure the reduction of error when inputs and/or hidden neurons
are added. In practice, it is assumed that this requirement is always true, but
usually it is false. In this paper, we propose a new algorithm that ensures
error decrease when input variables and/or hidden neurons are added to the
neural network. The behavior of two traditional algorithms and the
proposed algorithm in the forecast of Airline time series were compared.
The empirical results indicate that the proposed algorithm allows a steady
decrease of fit error in all cases, where de most important and differentiable
feature is the fact that reach values close to zero, which is not true for the
other algorithms. Therefore, it can be used as a suitable alternative
algorithm, especially when it needs a good fit.
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Palabras clave
Artificial Neural Networks (ANN), Time Series Forecasting, Learning Algorithms, Error Reduction