  We apply recent methods in stochastic data analysis for discovering a set of
few stochastic variables that represent the relevant information on a
multivariate stochastic system, used as input for artificial neural networks
models for air quality forecast. We show that using these derived variables as
input variables for training the neural networks it is possible to
significantly reduce the amount of input variables necessary for the neural
network model, without considerably changing the predictive power of the model.
The reduced set of variables including these derived variables is therefore
proposed as optimal variable set for training neural networks models in
forecasting geophysical and weather properties. Finally, we briefly discuss
other possible applications of such optimized neural network models.
