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

被引：30

作者：

Borrelli, A.

De Falco, I.

Della Cioppa, A.

Nicodemi, M. ^{[1
]}

Trautteur, G.

机构：

[1] Univ Naples Federico II, Dept Phys Sci, Ist Nazl Fis Nucl, Naples, Italy

[2] Natl Res Council Italy, ICAR, Naples, Italy

[3] Univ Salerno, DIIIE, Nat Computat Lab, Fisciano, SA, Italy

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2006年 / 370卷 / 01期

关键词：

multiobjective genetic programming; stochastic time series;

D O I：

10.1016/j.physa.2006.04.025

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：104 / 108

页数：5

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