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Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers

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  • Mak Kaboudan

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 modeling wavelet-conversions produces better forecasts than those from modeling 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.

Suggested Citation

  • Mak Kaboudan, 2006. "Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(9), pages 925-941.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:925-941
    DOI: 10.1080/02664760600744215
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    References listed on IDEAS

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