GENETIC PROGRAMMING AND NEURAL NETWORKS FORECASTING OF MONTHLY SUNSPOT NUMBERS

Mak Kaboudan ()
Additional contact information
Mak Kaboudan: School of Business, University of Redlands, Redlands, CA 92373, USA

New Mathematics and Natural Computation (NMNC), 2012, vol. 08, issue 02, 167-182

Abstract: A three-stage computational intelligence strategy is used to forecast the unsmoothed monthly sunspot number. The strategy employs agents that use two computational techniques, genetic programming (GP) and neural networks (NN), in a sequence of three stages. In the first, two agents fit the same set of observed monthly data. One employs GP, while the other employs NN. In the second, residuals (= differences between observed and solution values) from the first stage are fitted employing a different technique. The NN fitted-residuals are added to the GP first-stage solution while the GP fitted-residuals are added to the NN first-stage solution. In the third, outputs from the first and second stages become inputs to use in producing two new solutions that reconcile differences. The fittest third stage solution is then used to forecast 48 monthly sunspot numbers (September 2009 through August 2013). This modeling scheme delivered lower estimation errors at each stage. The next sunspot number peak is predicted to be around the middle of 2012.

Keywords: Sunspot numbers; solar cycle 24; genetic programming; neural networks (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S1793005712500020
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:wsi:nmncxx:v:08:y:2012:i:02:n:s1793005712500020

Ordering information: This journal article can be ordered from

DOI: 10.1142/S1793005712500020

Access Statistics for this article

New Mathematics and Natural Computation (NMNC) is currently edited by Paul P Wang

More articles in New Mathematics and Natural Computation (NMNC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().