Forecasting of Time Series Data Using Naturally Selected Bayesian Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Andrew John Novobilski",
  title =        "Forecasting of Time Series Data Using Naturally
                 Selected {Bayesian} Networks",
  school =       "University of Texas at Arlington",
  year =         "2000",
  address =      "USA",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "112 pages",
  abstract =     "Time series forecasting is a well-established area of
                 study supported by various methods for predicting a
                 future value from historical information. In addition
                 to continued research into methods originating from
                 numerical analysis, there is a growing interest in
                 methods that enhance numerical techniques by providing
                 a network-based infrastructure for describing the
                 organization of the series being forecast. A common
                 factor shared by network-based techniques such as
                 neural networks and Bayesian networks is their reliance
                 upon cognitive science to provide the inspiration for
                 the computational model the network represents. One
                 common problem they share is the automated
                 identification of the appropriate network structure
                 that is used to model the time series being

                 This dissertation addresses the problem of forecast
                 model identification by extending the use of cognitive
                 science techniques into a framework for the natural
                 selection of Bayesian networks. This framework consists
                 of a network model that has two components. The first
                 component is a genetic programming based representation
                 that decomposes into two parts; decision making and
                 information gathering. The decision making part is
                 represented as a Modified Naive Bayesian classifier.
                 The information gathering part is represented as an
                 attribute generator that classifies input values
                 according to numerical categories. The search process
                 for identifying the network components is done using
                 natural selection as implemented using genetic
                 programming. The second framework component uses the
                 resultant Bayesian model to forecast future values of a
                 given time series by using the maximum a posteriori
                 (MAP) hypothesis generated by querying the Bayesian

                 Using the framework for naturally selecting Bayesian
                 networks, experimental results are presented for both
                 synthesized time series and data sets drawn from the
                 daily values of individual stocks traded on the NASDAQ
                 and NYSE. Using a Random Walk as the benchmark
                 forecast, results from three variations of the
                 naturally selected Bayesian network forecaster with
                 single variable inputs are compared to results obtained
                 using a neural network. Additional information
                 detailing network structure and forecasting accuracy is
                 presented for the best models describing each time
                 series forecasted.",
  notes =        "UMI 9975303 supervised by Farhad Ahani Kamangar",

Genetic Programming entries for Andrew J Novobilski