Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{GeumYongLee:1999:aigp3,
  author =       "Geum Yong Lee",
  title =        "Genetic Recursive Regression for Modeling and
                 Forecasting Real-World Chaotic Time Series",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and 
                 Una-May O'Reilly and Peter J. Angeline",
  chapter =      "17",
  pages =        "401--423",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  URL =          "http://www.cs.bham.ac.uk/~wbl/aigp3/ch17.pdf",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.141.1197",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1197",
  abstract =     "I explore several extensions to genetic programming
                 for applications involving the forecasting of real
                 world chaotic time series. We first used Genetic
                 Symbolic Regression (GSR),which is the standard genetic
                 programming technique applied to the forecasting
                 problem in the same way that it is often applied to
                 symbolic regression problems [ Koza 1992, 1994]. We
                 observed that the performance of GSR depends on the
                 characteristics of the time series, and in particular
                 that it worked better for deterministic time series
                 than it did for stochastic or volatile time series.
                 Taking a hint from this observation, an assumption was
                 made in this study that the dynamics of a time series
                 comprise a deterministic and a stochastic part. By
                 subtracting the model built by GSR for the
                 deterministic part from the original time series, the
                 stochastic part would be obtained as a residual time
                 series. This study noted the possibility that GSR could
                 be used recursively to model the residual time series
                 of rather stochastic dynamics, which may still comprise
                 another deterministic and stochastic part. An algorithm
                 called GRR (Genetic Recursive Regression) has been
                 developed to apply GSR recursively to the sequence of
                 residual time series of stochastic dynamics, giving
                 birth to a sequence of sub-models for deterministic
                 dynamics extractable at each recursive application. At
                 each recursive application and after some termination
                 conditions are met, the submodels become the basis
                 functions for a series-expansion type representation of
                 a model. The numerical coefficients of the model are
                 calculated by the least square method with respect to
                 the predetermined region of the time series data set.
                 When the region includes the latest data set, the model
                 reflects the most recent changes in the dynamics of a
                 time series, thus increasing the forecasting
                 performance. This chapter shows how GRR has been
                 successfully applied to many real world chaotic time
                 series. The results are compared with those from other
                 GSR-like methods and various soft-computing
                 technologies such as neural networks. The results show
                 that GRR saves much computational effort while
                 achieving enhanced forecasting performance for several
                 selected problems.",
  notes =        "AiGP3",
  size =         "23 pages",
}

Genetic Programming entries for Geum Yong Lee

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