Genetic Programming And Neural Networks Forecasting Of Monthly Sunspot Numbers
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 08 (2012)
Issue (Month): 02 ()
|Contact details of provider:|| Web page: http://www.worldscinet.com/nmnc/nmnc.shtml|
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:wsi:nmncxx:v:08:y:2012:i:02:p:167-182. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tai Tone Lim)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.