Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

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@Article{Lee201166,
  author =       "Yi-Shian Lee and Lee-Ing Tong",
  title =        "Forecasting time series using a methodology based on
                 autoregressive integrated moving average and genetic
                 programming",
  journal =      "Knowledge-Based Systems",
  volume =       "24",
  number =       "1",
  pages =        "66--72",
  year =         "2011",
  ISSN =         "0950-7051",
  DOI =          "doi:10.1016/j.knosys.2010.07.006",
  URL =          "http://www.sciencedirect.com/science/article/B6V0P-50JHBSY-1/2/1501a7c1121cbfcf9683f1a0d781806b",
  keywords =     "genetic algorithms, genetic programming, ARIMA, Hybrid
                 model, Forecasting, Artificial neural network",
  abstract =     "The autoregressive integrated moving average (ARIMA),
                 which is a conventional statistical method, is employed
                 in many fields to construct models for forecasting time
                 series. Although ARIMA can be adopted to obtain a
                 highly accurate linear forecasting model, it cannot
                 accurately forecast nonlinear time series. Artificial
                 neural network (ANN) can be used to construct more
                 accurate forecasting model than ARIMA for nonlinear
                 time series, but explaining the meaning of the hidden
                 layers of ANN is difficult and, moreover, it does not
                 yield a mathematical equation. This study proposes a
                 hybrid forecasting model for nonlinear time series by
                 combining ARIMA with genetic programming (GP) to
                 improve upon both the ANN and the ARIMA forecasting
                 models. Finally, some real data sets are adopted to
                 demonstrate the effectiveness of the proposed
                 forecasting model.",
}

Genetic Programming entries for Yi-Shian Lee Lee-Ing Tong

Citations