Empirical Analysis of Model Selection Criteria for Genetic Programming in Modeling of Time Series System

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "A. Garg and S. Sriram and K. Tai",
  title =        "Empirical Analysis of Model Selection Criteria for
                 Genetic Programming in Modeling of Time Series System",
  booktitle =    "2013 IEEE Symposium Series on Computational
  year =         "2013",
  editor =       "P. N. Suganthan",
  pages =        "90--94",
  address =      "Singapore",
  month =        "16-19 " # apr,
  keywords =     "genetic algorithms, genetic programming, AIC, FPE,
                 PRESS, fitness function, model selection, stock
  DOI =          "doi:10.1109/CIFEr.2013.6611702",
  size =         "5 pages",
  abstract =     "Genetic programming (GP) and its variants have been
                 extensively applied for modelling of the stock markets.
                 To improve the generalisation ability of the model, GP
                 have been hybridised with its own variants (gene
                 expression programming (GEP), multi expression
                 programming (MEP)) or with the other methods such as
                 neural networks and boosting. The generalisation
                 ability of the GP model can also be improved by an
                 appropriate choice of model selection criterion. In the
                 past, several model selection criteria have been
                 applied. In addition, data transformations have
                 significant impact on the performance of the GP models.
                 The literature reveals that few researchers have paid
                 attention to model selection criterion and data
                 transformation while modelling stock markets using GP.
                 The objective of this paper is to identify the most
                 appropriate model selection criterion and
                 transformation that gives better generalised GP models.
                 Therefore, the present work will conduct an empirical
                 analysis to study the effect of three model selection
                 criteria across two data transformations on the
                 performance of GP while modelling the stock indexed in
                 the New York Stock Exchange (NYSE). It was found that
                 FPE criteria have shown a better fit for the GP model
                 on both data transformations as compared to other model
                 selection criteria.",
  notes =        "CIFEr 2013, also known as \cite{6611702}",

Genetic Programming entries for Akhil Garg Sriram Srivatsav Kang Tai