Time Series Modeling and Prediction Using Postfix Genetic Programming

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

@InProceedings{Dabhi:2014:ACCT,
  author =       "Vipul K. Dabhi and Sanjay Chaudhary",
  booktitle =    "Fourth International Conference on Advanced Computing
                 Communication Technologies (ACCT 2014)",
  title =        "Time Series Modeling and Prediction Using Postfix
                 Genetic Programming",
  year =         "2014",
  month =        feb,
  pages =        "307--314",
  keywords =     "genetic algorithms, genetic programming, series
                 Modelling, Postfix Genetic Programming, One-step ahead
                 prediction, Multi-step ahead prediction",
  DOI =          "doi:10.1109/ACCT.2014.33",
  size =         "8 pages",
  abstract =     "Traditional techniques for time series modelling can
                 capture linear behaviour of data and lack the ability
                 to identify nonlinear patterns in time series.
                 Therefore, machine learning techniques like Neural
                 Network or Genetic Programming (GP) are used by
                 practitioners for modelling nonlinear and irregular
                 time series. GP is preferred over other techniques
                 because it does not presume model structure a priori.
                 This paper introduces the use of Postfix-GP, a postfix
                 notation based GP, for real-world nonlinear time series
                 modelling problems. The Postfix-GP uses linear genome
                 representation and stack based evaluation to reduce
                 space-time complexity of GP. The Postfix-GP is applied
                 on two real time series modelling problems: sunspots
                 and river flow series. Performance of evolved
                 Postfix-GP models on training data and out-of-sample
                 data are compared with those obtained by others using
                 EGIPSYS. The obtained results indicate that Postfix-GP
                 offers a new possibility for solving time series
                 modelling and prediction problems.",
  notes =        "Also known as \cite{6783469}",
}

Genetic Programming entries for Vipul K Dabhi Sanjay Chaudhary

Citations