Evaluation Of Forecasts Produced By Genetically Evolved Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "M. A. Kaboudan",
  title =        "Evaluation Of Forecasts Produced By Genetically
                 Evolved Models",
  booktitle =    "Computing in Economics and Finance",
  year =         "2000",
  address =      "Universitat Pompeu Fabra, Barcelona, Spain",
  month =        "6-8 " # jul,
  organisation = "Society for Computational Economics",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://fmwww.bc.edu/cef00/papers/paper331.pdf",
  size =         "33 pages",
  abstract =     "Genetic programming (or GP) is a random search
                 technique that emerged in the late 1980s and early
                 1990s. A formal description of the method was
                 introduced in Koza (1992). GP applies to many
                 optimisation areas. One of them is modelling time
                 series and using those models in forecasting. Unlike
                 other modeling techniques, GP is a computer program
                 that 'searches' for a specification that replicates the
                 dynamic behaviour of observed series. To use GP, one
                 provides operators (such as +, -, *, ?, exp, log, sin,
                 cos, ... etc.) and identifies as many variables thought
                 best to reproduce the dependent variable's dynamics.
                 The program then randomly assembles equations with
                 different specifications by combining some of the
                 provided variables with operators and identifies that
                 specification with the minimum sum of squared errors
                 (or SSE). This process is an iterative evolution of
                 successive generations consisting of thousands of the
                 assembled equations where only the fittest within a
                 generation survive to breed better equations also using
                 random combinations until the best one is found.
                 Clearly from this simple description, the method is
                 based on heuristics and has no theoretical foundation.
                 However, resulting final equations seem to produce
                 reasonably accurate forecasts that compare favourably
                 to forecasts humanly conceived specifications produce.
                 With encouraging results difficult to overlook or
                 ignore, it is important to investigate GP as a
                 forecasting methodology. This paper attempts to
                 evaluate forecasts genetically evolved models (or GEMs)
                 produce for experimental data as well as real world
                 time series.The organisation of this paper in four
                 Sections. Section 1 contains an overview of GEMs. The
                 reader will find lucid explanation of how models are
                 evolved using genetic methodology as well as features
                 found to characterise GEMs as a modeling technique.
                 Section 2 contains descriptions of simulated and real
                 world data and their respective fittest identified
                 GEMs. The MSE and a new alpha-statistic are presented
                 to compare models' performances. Simulated data were
                 chosen to represent processes with different behavioral
                 complexities including linear, linear-stochastic,
                 nonlinear, nonlinear chaotic, and nonlinear-stochastic.
                 Real world data consist of two time series popular in
                 analytical statistics: Canadian lynx data and sunspot
                 numbers. Predictions of historic values of each series
                 (used in generating the fittest model) are also
                 presented there. Forecasts and their evaluations are in
                 Section 3. For each series, single- and multi-step
                 forecasts are evaluated according to the mean squared
                 error, normalised mean squared error, and alpha-
                 statistic. A few concluding remarks are in the
                 discussion in Section 4.",
  notes =        "22 August 2004
                 http://ideas.repec.org/p/sce/scecf0/331.html CEF number

Genetic Programming entries for Mahmoud A Kaboudan