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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