Genetic Programming based approach for Modeling Time Series data of real systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Dilip P. Ahalpara and Jitendra C. Parikh",
  title =        "Genetic Programming based approach for Modeling Time
                 Series data of real systems",
  journal =      "International Journal of Modern Physics C,
                 Computational Physics and Physical Computation",
  year =         "2008",
  volume =       "19",
  number =       "1",
  pages =        "63--91",
  keywords =     "genetic algorithms, genetic programming, Time series
                 analysis, artificial neural networks",
  DOI =          "doi:10.1142/S0129183108011942",
  abstract =     "Analytic models of a computer generated time series
                 (logistic map) and three real time series (ion
                 saturation current in Aditya Tokamak plasma, NASDAQ
                 composite index and Nifty index) are constructed using
                 Genetic Programming (GP) framework. In each case, the
                 optimal map that results from fitting part of the data
                 set also provides a very good description of the rest
                 of the data. Predictions made using the map iteratively
                 are very good for computer generated time series but
                 not for the data of real systems. For such cases, an
                 extended GP model is proposed and illustrated. A
                 comparison of these results with those obtained using
                 Artificial Neural Network (ANN) is also carried out.",
  notes =        "IJMPC PACS numbers: 05.45.Tp, 02.30.NW

                 Institute for Plasma Research, Near Indira Bridge,
                 Bhat, Gandhinagar-382428, India

                 Physical Research Laboratory, Navrangpura,
                 Ahmedabad-380009, India",

Genetic Programming entries for Dilip P Ahalpara Jitendra C Parikh