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

Author

Listed:
  • Borrelli, A.
  • De Falco, I.
  • Della Cioppa, A.
  • Nicodemi, M.
  • Trautteur, G.

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.

Suggested Citation

  • Borrelli, A. & De Falco, I. & Della Cioppa, A. & Nicodemi, M. & Trautteur, G., 2006. "Performance of genetic programming to extract the trend in noisy data series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 104-108.
    • Handle: RePEc:eee:phsmap:v:370:y:2006:i:1:p:104-108
    DOI: 10.1016/j.physa.2006.04.025
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    Cited by:

    1. Mehdi Dasineh & Amir Ghaderi & Mohammad Bagherzadeh & Mohammad Ahmadi & Alban Kuriqi, 2021. "Prediction of Hydraulic Jumps on a Triangular Bed Roughness Using Numerical Modeling and Soft Computing Methods," Mathematics, MDPI, vol. 9(23), pages 1-24, December.

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