Forecasting with computer-evolved model specifications: a genetic programming application

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

  author =       "M. A. Kaboudan",
  title =        "Forecasting with computer-evolved model
                 specifications: a genetic programming application",
  journal =      "Computers and Operations Research",
  year =         "2003",
  volume =       "30",
  number =       "11",
  pages =        "1661--1681",
  month =        sep,
  email =        "",
  keywords =     "genetic algorithms, genetic programming, Computational
                 methods, Nonlinear dynamic systems, Time series,
                 Sunspot numbers",
  URL =          "",
  ISSN =         "0305-0548",
  DOI =          "doi:10.1016/S0305-0548(02)00098-9",
  abstract =     "This paper uses genetic programming (GP) to evolve
                 model specifications of time series data. GP is a
                 computerized random search optimisation algorithm that
                 assembles equations until it identifies the fittest
                 one. The technique is applied here to artificially
                 simulated data first then to real-world sunspot
                 numbers. One-step-ahead forecasts produced by the
                 fittest of computer-evolved models are evaluated and
                 compared with alternatives. The results suggest that GP
                 may produce reasonable forecasts if their user selects
                 appropriate input variables and comprehends the process
                 investigated. Further, the technique appears promising
                 in forecasting noisy complex series perhaps better than
                 other existing methods. It is suitable for decision
                 makers who set high priority on obtaining accurate
                 forecasts rather than on probing into and approximating
                 the underlying data generating process.

                 This paper contains a brief introduction and an
                 evaluation of the use of genetic programming (GP) in
                 forecasting time series. GP is a computerized random
                 search optimization technique based upon Darwin's
                 theory of evolution. The algorithm is first applied to
                 model and forecast artificially simulated linear and
                 nonlinear time series. Results are used to evaluate the
                 effectiveness of GP as a forecasting technique. It is
                 then applied to model and forecast sunspot numbers--the
                 most frequently analyzed and forecasted series. An
                 autoregressive and a threshold nonlinear dynamical
                 systems to capture the dynamics of the irregular
                 sunspot numbers' cycle were tested using GP. The latter
                 delivered estimated equations yielding the lowest mean
                 square error ever reported for the series. This paper
                 demonstrates that GP's forecasting capabilities depend
                 on the structure and complexity of the process to
                 model. Skills and intuition of GP's user are its

Genetic Programming entries for Mahmoud A Kaboudan