Forecasting with genetically programmed polynomial neural networks

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 comprehens...

Full description

Saved in:
Bibliographic Details
Published in:International journal of forecasting 2006-04, Vol.22 (2), p.249-265
Main Authors: de Menezes, Lilian M., Nikolaev, Nikolay Y.
Format: Article
Language:English
Subjects:
Algorithms
Forecasting techniques
Genetic programming
Neural networks
Nonlinear models
Polynomials
Statistical learning algorithms
Studies
Tree-structured polynomial neural network models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 regularized 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.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2005.05.002