Abstract
This paper investigates the possibility of obtaining long-into-the-future reliable forecasts of observed nonlinear cyclical phenomena. Unsmoothed monthly sunspot numbers that are characteristically cyclical with nonlinear dynamics as well as their wavelet-transformed and wavelet-denoised series are forecasted through October 2008. The objective is to determine whether modelling wavelet-conversions of a series provides reasonable forecasts. Two computational techniques –neural networks and genetic programming –are used to model the dynamics of the series. Statistical comparison of their ex post forecasts is then used to identify the data set and computational technique to use under the circumstances.
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