A Two Tiered Cognitive Model for the Forecasting of Time Series Data

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

  author =       "Andrew J. Novobilski and Farhad A. Kamangar",
  title =        "A Two Tiered Cognitive Model for the Forecasting of
                 Time Series Data",
  booktitle =    "Second International ICSC Symposium on Neural
  year =         "2000",
  address =      "Berlin, Germany",
  month =        may # " 23-26",
  publisher =    "NAISO Academic Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "3-906454-21-5",
  size =         "6 pages",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  abstract =     "This paper describes two mutually enhancing
                 technologies that will be used to evolve Bayesian
                 network based forecasting models;. human/artificial
                 cognition and Bayesian networks. A two tiered
                 representation is introduced which mimics the way the
                 human brain is thought to organise itself. This
                 representation can be manipulated using genetic
                 programming techniques to extract both attributes and
                 organisation of a Bayesian Network that models the
                 underlying stochastic process for time series data.
                 Experimental results are presented that demonstrate the
                 effectiveness of the method in forecasting daily prices
                 of stock issues.",
  notes =        "http://www.icsc-naiso.org/publications/list_nc00.html",

Genetic Programming entries for Andrew J Novobilski Farhad A Kamangar