Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Neal Wagner and Zbigniew Michalewicz and 
                 Moutaz Khouja and Rob Roy McGregor",
  title =        "Time Series Forecasting for Dynamic Environments: The
                 {DyFor} Genetic Program Model",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2007",
  volume =       "11",
  number =       "4",
  pages =        "433--452",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Dynamic,
                 forecasting, parameter adaptation, time series",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1109/TEVC.2006.882430",
  size =         "20 pages",
  abstract =     "Several studies have applied genetic programming (GP)
                 to the task of forecasting with favourable results.
                 However, these studies, like those applying other
                 techniques, have assumed a static environment, making
                 them unsuitable for many real-world time series which
                 are generated by varying processes. This study
                 investigates the development of a new dynamic GP model
                 that is specifically tailored for forecasting in
                 nonstatic environments. This Dynamic Forecasting
                 Genetic Program (DyFor GP) model incorporates features
                 that allow it to adapt to changing environments
                 automatically as well as retain knowledge learned from
                 previously encountered environments. The DyFor GP model
                 is tested for forecasting efficacy on both simulated
                 and actual time series including the U.S. Gross
                 Domestic Product and Consumer Price Index Inflation.
                 Results show that the performance of the DyFor GP model
                 improves upon that of benchmark models for all
                 experiments. These findings highlight the DyFor GP's
                 potential as an adaptive, nonlinear model for
                 real-world forecasting applications and suggest further

Genetic Programming entries for Neal Wagner Zbigniew Michalewicz Moutaz Khouja Rob Roy McGregor