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

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@InProceedings{Novobilski:2000:NC,

• 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 Computation",
• 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:10.1.1.137.9431",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.9431",
• 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

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