Performance of genetic programming to extract the trend in noisy data series

A. Borrelli, I. De Falco, A. Della Cioppa, M. Nicodemi and G. Trautteur

Physica A: Statistical Mechanics and its Applications, 2006, vol. 370, issue 1, 104-108

Abstract: 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.

Keywords: Multiobjective genetic programming; Stochastic time series (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:370:y:2006:i:1:p:104-108

DOI: 10.1016/j.physa.2006.04.025

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