DocumentCode
1619754
Title
Synthesizing missing data points with iterated function systems
Author
Maze, David S. ; Sirgany, Wadie N.
Author_Institution
IBM Federal Syst. Co., Manassas, VA, USA
fYear
1992
Firstpage
583
Abstract
A current application for fractals and iterated function systems (IFSs) is data compression. A new use for fractals and IFSs, namely, synthesizing missing examples in given data with a self-affine fractal model, is explored. The authors discuss how to find the IFS model parameters required to closely approximate the given data as well as using the IFS model to estimate the missing samples. Applications of this work to a mountain profile and stock market data are presented
Keywords
financial data processing; fractals; function approximation; geographic information systems; iterative methods; stock markets; IFS model parameters; data points; fractals; iterated function systems; mountain profile; self-affine fractal model; stock market data; Data compression; Filling; Fractals; Interpolation; Stock markets;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1992., Proceedings of the 35th Midwest Symposium on
Conference_Location
Washington, DC
Print_ISBN
0-7803-0510-8
Type
conf
DOI
10.1109/MWSCAS.1992.271256
Filename
271256
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