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.
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