Long memory time series forecasting by using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{CarrenoJara:2011:GPEM,
  author =       "Emiliano {Carreno Jara}",
  title =        "Long memory time series forecasting by using genetic
                 programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2012",
  volume =       "12",
  number =       "4",
  pages =        "429--456",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Long memory,
                 Time series forecasting, Multi-objective search, ARFIMA
                 models",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-011-9140-7",
  size =         "28 pages",
  abstract =     "Real-world time series have certain properties, such
                 as stationarity, seasonality, linearity, among others,
                 which determine their underlying behaviour. There is a
                 particular class of time series called long-memory
                 processes, characterised by a persistent temporal
                 dependence between distant observations, that is, the
                 time series values depend not only on recent past
                 values but also on observations of much prior time
                 periods. The main purpose of this research is the
                 development, application, and evaluation of a
                 computational intelligence method specifically tailored
                 for long memory time series forecasting, with emphasis
                 on many-step-ahead prediction. The method proposed here
                 is a hybrid combining genetic programming and the
                 fractionally integrated (long-memory) component of
                 autoregressive fractionally integrated moving average
                 (ARFIMA) models. Another objective of this study is the
                 discovery of useful comprehensible novel knowledge,
                 represented as time series predictive models. In this
                 respect, a new evolutionary multi-objective search
                 method is proposed to limit complexity of evolved
                 solutions and to improve predictive quality. Using
                 these methods allows for obtaining lower complexity
                 (and possibly more comprehensible) models with high
                 predictive quality, keeping run time and memory
                 requirements low, and avoiding bloat and over-fitting.
                 The methods are assessed on five real-world long memory
                 time series and their performance is compared to that
                 of statistical models reported in the literature.
                 Experimental results show the proposed methods'
                 advantages in long memory time series forecasting.",
  notes =        "River Nile flow, Radial basis function, finance UK
                 inflation rate. FI-GP. Long-memory variables. RBF-GP.
                 fractional Gaussian Model

                 encapsulation, lags. GPC++ version 0.40",
}

Genetic Programming entries for Emiliano J Carreno

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