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.

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Year of publication:	2006
Authors:	Borrelli, A. ; De Falco, I. ; Della Cioppa, A. ; Nicodemi, M. ; Trautteur, G.
Published in:	Physica A: Statistical Mechanics and its Applications. - Elsevier, ISSN 0378-4371. - Vol. 370.2006, 1, p. 104-108
Publisher:	Elsevier
Subject:	Multiobjective genetic programming \| Stochastic time series

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

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