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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{Kaboudan:2004:efmaci,

• 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 algorithms",
• URL = "http://www.aiecon.org/conference/efmaci2004/pdf/GP_Basics_paper.pdf",
• URL = "http://www.aiecon.org/conference/efmaci2004/pdf/GP_Basics_ppt.pdf",
• 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 models.",
• notes = "Taiwan's National Science Counsel and AI-Econ Research Center",

}

Genetic Programming entries for Mahmoud A Kaboudan

Citations