Computational Forecasting of Wavelet-Converted Monthly Sunspot Numbers

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@Article{Kaboudan:2006:JAS,
  author =       "Mak Kaboudan",
  title =        "Computational Forecasting of Wavelet-Converted Monthly
                 Sunspot Numbers",
  journal =      "Journal of Applied Statistics",
  year =         "2006",
  volume =       "33",
  number =       "9",
  pages =        "925--941",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Wavelets,
                 thresholding, neural networks, sunspot numbers",
  ISSN =         "0266-4763",
  DOI =          "doi:10.1080/02664760600744215",
  abstract =     "Monthly average sunspot numbers follow irregular
                 cycles with complex nonlinear dynamics. Statistical
                 linear models constructed to forecast them are
                 therefore inappropriate while nonlinear models produce
                 solutions sensitive to initial conditions. Two
                 computational techniques 'neural networks' and 'genetic
                 programming' that have their advantages are applied
                 instead to the monthly numbers and their
                 wavelet-transformed and wavelet-denoised series. The
                 objective is to determine if modelling
                 wavelet-conversions produces better forecasts than
                 those from modeling a series' observed values. Because
                 sunspot numbers are indicators of geomagnetic activity
                 their forecast is important. Geomagnetic storms
                 endanger satellites and disrupt communications and
                 power systems on Earth.",
  notes =        "http://www.tandf.co.uk/journals/titles/02664763.asp",
}

Genetic Programming entries for Mahmoud A Kaboudan

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