%0 Journal Article %1 deMenezes:Fwg:06 %A de Menezes, Lilian M. %A Nikolaev, Nikolay Y. %D 2006 %J International Journal of Forecasting %K Nonlinear Statistical Tree-structured algorithms algorithms, genetic learning models, network neural polynomial programming, %N 2 %P 249--265 %R doi:10.1016/j.ijforecast.2005.05.002 %T Forecasting with genetically programmed polynomial neural networks %V 22 %X Recent literature on nonlinear models has shown genetic programming to be a potential tool for forecasters. A special type of genetically programmed model, namely polynomial neural networks, is addressed. Their outputs are polynomials and, as such, they are open boxes that are amenable to comprehension, analysis, and interpretation. This paper presents a polynomial neural network forecasting system, PGP, which has three innovative features: polynomial block reformulation, local ridge regression for weight estimation, and regularised weight subset selection for pruning that uses a least absolute shrinkage and selection operator. The relative performance of this system to other established forecasting procedures is the focus of this research and is illustrated by three empirical studies. Overall, the results are very promising and indicate areas for further research.

@article{deMenezes:Fwg:06, abstract = {Recent literature on nonlinear models has shown genetic programming to be a potential tool for forecasters. A special type of genetically programmed model, namely polynomial neural networks, is addressed. Their outputs are polynomials and, as such, they are open boxes that are amenable to comprehension, analysis, and interpretation. This paper presents a polynomial neural network forecasting system, PGP, which has three innovative features: polynomial block reformulation, local ridge regression for weight estimation, and regularised weight subset selection for pruning that uses a least absolute shrinkage and selection operator. The relative performance of this system to other established forecasting procedures is the focus of this research and is illustrated by three empirical studies. Overall, the results are very promising and indicate areas for further research.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {{de Menezes}, Lilian M. and Nikolaev, Nikolay Y.}, biburl = {https://www.bibsonomy.org/bibtex/26e36d11564c94490ee4f6daa174699a1/brazovayeye}, doi = {doi:10.1016/j.ijforecast.2005.05.002}, interhash = {de3818d112607569137162504c7d7f30}, intrahash = {6e36d11564c94490ee4f6daa174699a1}, journal = {International Journal of Forecasting}, keywords = {Nonlinear Statistical Tree-structured algorithms algorithms, genetic learning models, network neural polynomial programming,}, month = {April-June}, number = 2, pages = {249--265}, timestamp = {2008-06-19T17:38:34.000+0200}, title = {Forecasting with genetically programmed polynomial neural networks}, volume = 22, year = 2006 }