Research on Time Series Modeling by Genetic Programming and Model De-noising

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{oai:CiteSeerX.psu:10.1.1.522.8848,

• author = "Yongqiang Zhang and Lili Wu",
• title = "Research on Time Series Modeling by Genetic Programming and Model De-noising",
• booktitle = "Proceedings of the 2007 WSEAS International Conference on Computer Engineering and Applications",
• year = "2007",
• address = "Gold Coast, Australia",
• month = jan # " 17-19",
• publisher = "WSEAS",
• keywords = "genetic algorithms, genetic programming, denoising, wavelet threshold, time series, modelling, gp model",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.522.8848",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.522.8848",
• URL = "http://www.wseas.us/e-library/conferences/2007australia/papers/550-117.pdf",
• size = "5 pages",
• abstract = "In order to cast off the subjective assumptions of traditional methods for modelling, this paper brings forward the Genetic Programming (GP for short) algorithm to establish a reasonable system model dynamically for time series signal. Meanwhile, the approach of wavelet threshold is adopted to de-noising for the GP models. On the basis of these theories, the simulation experimentations about two instances are carried on. The results indicate that the threshold approach of wavelet de-noising for time series signal models take on better impacts, which can improve the GP models to some extent, and enhance the forecast precision of the model.",

}

Genetic Programming entries for Yongqiang Zhang Lili Wu

Citations