Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming

被引：2
|
作者：
Bernal-Urbina, M. [1 ,2 ]
Flores-Mendez, A. [2 ,3 ]
机构：
[1] La Salle Univ, Mexico City, DF, Mexico
[2] La Salle Univ, Res & Dev Lab, Mexico City, DF, Mexico
[3] CINVESTAV, IPN, Dept Math, Mexico City, DF, Mexico
来源：
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 | 2008年
关键词：
D O I：
10.1109/IJCNN.2008.4634270
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analyzed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimization algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm.
引用
收藏
页码：3325 / +
页数：2
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