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solution space because of their performance. When
there is a few amount of input data, the CHC and
clearing algorithms overfit the data and the results
are worse than if genetic algorithms were used. This
is what happens in our case: although the mean
square error obtained with genetic algorithms is
worse than that obtained with the other algorithms,
the adjustment of real and prediction data points is
better at a general stage and prediction is therefore
more trustworthy. Figures 3-4 show an example of
GDP prediction with CHC and genetic algorithms,
for the community of Andalucia.
6 CONCLUSIONS
In this paper, we have studied a set of evolutionary
models to train an Elman recurrent neural network,
applied to time series prediction. These models have
proved to be a good tool to predict Spanish
autonomous indebtedness. Furthermore, genetic
algorithms enable Elman networks to be trained
easily, and prediction to be the most approximate
possible. The average MSE obtained between each
community is 0.0116. This means that not only is
prediction good, but also that the model works
uniformly in every community. The standard
deviation of output data is 0.011056, which
corroborates what we have just said.
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Andalucía
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1 3 5 7 9 11 13 15
Predicción
PIB Andalucía
Figure 4: GDP prediction for Andalucia with GA
Andalucía
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1 3 5 7 9 11 13 15
Predicción
PIB Andalucía
Figure 3: GDP prediction for Andalucia with CHC
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