Improved forecasting of time series data of real system using genetic programming

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

@InProceedings{Ahalpara:2010:gecco,
  author =       "Dilip P. Ahalpara",
  title =        "Improved forecasting of time series data of real
                 system using genetic programming",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "977--978",
  keywords =     "genetic algorithms, genetic programming, Poster",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830658",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A study is made to improve short term forecasting of
                 time series data of real system using Genetic
                 Programming (GP) under the framework of time delayed
                 embedding technique. GP based approach is used to make
                 analytical model of time series data of real system
                 using embedded vectors that help reconstruct the phase
                 space. The map equations, involving non-linear symbolic
                 expressions in the form of binary trees comprising of
                 time delayed components in the immediate past, are
                 first obtained by carrying out single-step GP fit for
                 the training data set and usually they are found to
                 give good fitness as well as single-step predictions.
                 However while forecasting the time series based on
                 multi-step predictions in the out-of-sample region in
                 an iterative manner, these solutions often show rapid
                 deterioration as we dynamically forward the solution in
                 future time. With a view to improve on this limitation,
                 it is shown that if the multi-step aspect is
                 incorporated while making the GP fit itself, the
                 corresponding GP solutions give multi-step predictions
                 that are improved to a fairly good extent for around
                 those many multi-steps as incorporated during the
                 multi-step GP fit. Two different methods for multi-step
                 fit are introduced, and the corresponding prediction
                 results are presented. The modified method is shown to
                 make better forecast for out-of-sample multi-step
                 predictions for the time series of a real system,
                 namely Electroencephelograph (EEG) signals.",
  notes =        "Also known as \cite{1830658} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",
}

Genetic Programming entries for Dilip P Ahalpara

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