Performance of Genetic Programming to extract the trend in noisy data series

In this paper an approach based on Genetic Programming for forecasting stochastic time series is outlined. To obtain a suitable test–bed some well known time series are dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the method is applied to the MIB30 Index series.