@article{Borrelli:2006:PhysicaA,
title = {Performance of genetic programming to extract the
trend in noisy data series},
author = {A. Borrelli and I. {De Falco} and A. {Della Cioppa} and M. Nicodemi and G. Trautteura},
journal = {Physica A: Statistical and Theoretical Physics},
month = {1 October},
note = {Econophysics Colloquium - Proceedings of the
International Conference {"}Econophysics
Colloquium{"}},
number = {1},
pages = {104--108},
volume = {370},
year = {2006},
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.},
doi = {doi:10.1016/j.physa.2006.04.025},
keywords = {Multiobjective Stochastic algorithms, genetic programming, series time }
}
::