GP Basics / A Measure of Time Series' Predictability Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Mak Kaboudan",
  title =        "GP Basics / A Measure of Time Series' Predictability
                 Using Genetic Programming",
  howpublished = "Tutorial at Computational Intelligence in Economics
                 and Finance, Summer Workshop",
  year =         "2004",
  month =        "16 " # aug,
  address =      "Taiwan",
  keywords =     "genetic algorithms, genetic programming, Complexity,
                 Nonlinearity, Artificial intelligence, Search
  URL =          "",
  URL =          "",
  size =         "24 pages",
  abstract =     "Based on standard genetic programming (GP) paradigm,
                 we introduce a new test of time series predictability.
                 It is an index computed as the ratio of two fitness
                 values from GP runs when searching for a series data
                 generating process. One value belongs to the original
                 series, while the other belongs to the same series
                 after it is randomly shuffled. Theoretically, the index
                 boundaries are between zero and 100, where zero
                 characterizes stochastic processes while 100 typifies
                 predictability. This test helps in reducing model
                 search space and in producing more reliable forecast
  notes =        "Taiwan's National Science Counsel and AI-Econ Research

Genetic Programming entries for Mahmoud A Kaboudan