In this paper an approach based on Genetic Programming for forecasting stochastic time series is outlined. To obtain a suitable testbed 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.
Performance of Genetic Programming to extract the trend in noisy data series
De Falco I;
2006
Abstract
In this paper an approach based on Genetic Programming for forecasting stochastic time series is outlined. To obtain a suitable testbed 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.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
