Research on time series modeling by genetic programming and model de-noising

  • Authors:

  • Yongqiang Zhang;Lili Wu

  • Affiliations:

  • School of Information and Electricity-Engineering, Hebei University of Engineering, Handan, Hebei Province, P.R.China;School of Information and Electricity-Engineering, Hebei University of Engineering, Handan, Hebei Province, P.R.China

  • Venue:
  • CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
  • Year:
  • 2007

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Abstract

In order to cast off the subjective assumptions of traditional methods for modeling, this paper brings forward the Genetic Programming (GP for short) algorithm to establish a reasonable system model dynamically for time series signal. Meanwhile, the approach of wavelet threshold is adopted to de-noising for the GP models. On the basis of these theories, the simulation experimentations about two instances are carried on. The results indicate that the threshold approach of wavelet de-noising for time series signal models take on better impacts, which can improve the GP models to some extent, and enhance the forecast precision of the model.