Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers

Mak Kaboudan

Journal of Applied Statistics, 2006, vol. 33, issue 9, 925-941

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.

Keywords: Wavelets; thresholding; neural networks; genetic programming; sunspot numbers (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760600744215 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:33:y:2006:i:9:p:925-941

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760600744215

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().