Research on Time Series Modeling by Genetic Programming and Model De-noising

Created by W.Langdon from gp-bibliography.bib Revision:1.4020

  author =       "Yongqiang Zhang and Lili Wu",
  title =        "Research on Time Series Modeling by Genetic
                 Programming and Model De-noising",
  booktitle =    "Proceedings of the 2007 WSEAS International Conference
                 on Computer Engineering and Applications",
  year =         "2007",
  address =      "Gold Coast, Australia",
  month =        jan # " 17-19",
  publisher =    "WSEAS",
  keywords =     "genetic algorithms, genetic programming, denoising,
                 wavelet threshold, time series, modelling, gp model",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  URL =          "",
  URL =          "",
  size =         "5 pages",
  abstract =     "In order to cast off the subjective assumptions of
                 traditional methods for modelling, 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.",

Genetic Programming entries for Yongqiang Zhang Lili Wu