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
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;
Conference_Titel :
Circuits and Systems, 1992., Proceedings of the 35th Midwest Symposium on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-0510-8
DOI :
10.1109/MWSCAS.1992.271256
