A Two Tiered Cognitive Model for the Forecasting of Time Series Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Novobilski:2000:NC,
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author = "Andrew J. Novobilski and Farhad A. Kamangar",
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title = "A Two Tiered Cognitive Model for the Forecasting of
Time Series Data",
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booktitle = "Second International ICSC Symposium on Neural
Computation",
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year = "2000",
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address = "Berlin, Germany",
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month = may # " 23-26",
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publisher = "NAISO Academic Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "3-906454-21-5",
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size = "6 pages",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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contributor = "CiteSeerX",
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language = "en",
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oai = "oai:CiteSeerXPSU:10.1.1.137.9431",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.9431",
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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.",
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notes = "http://www.icsc-naiso.org/publications/list_nc00.html",
- }
Genetic Programming entries for
Andrew J Novobilski
Farhad A Kamangar
Citations