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Proceedings Paper

Detection and modeling of chaotic dynamics using wavelet techniques
Author(s): Jeffrey D. Scargle
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Paper Abstract

Powerful new data analysis techniques based on wavelets are proving extremely useful in the reduction and interpretation of time series data. The goals of these methods include denoising, characterizing, modeling, and compressing of time series data. The multi-scale nature of wavelet analysis makes it especially useful for detection and characterization of self-similar or 'scaling' behavior, such as is common for chaotic processes. This paper describes how wavelet techniques led to a transient-chaos model for a rapidly fluctuating celestial X-ray source. The methods described here are freely available in a new software package called TeachWave, developed by David Donoho and Iain Johnstone of Stanford University (anonymous ftp to playfair.stanford.edu; the software is in directory /pub/software/wavelets, and a number of related technical papers are in /pub/reports).

Paper Details

Date Published: 15 March 1994
PDF: 9 pages
Proc. SPIE 2242, Wavelet Applications, (15 March 1994); doi: 10.1117/12.170048
Show Author Affiliations
Jeffrey D. Scargle, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 2242:
Wavelet Applications
Harold H. Szu, Editor(s)

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