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Optical Engineering

Resolving time-series structure with a controlled wavelet transform
Author(s): Steven J. Schiff
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Paper Abstract

Wavelet transforms are powerful techniques that can decompose time series into both time and frequency components. Their application to experimental data has been hindered by the lack of a straightforward method to handle noise. A noise reduction technique, developed recently for use in wavelet cluster analysis in cosmology and astronomy, is adapted here for time-series data. Noise is filtered using control surrogate data sets generated from randomized aspects of the original time series. The method is a powerful extension of the wavelet transform that is readily applied to the detection of structure in stationary and nonstationary time series.

Paper Details

Date Published: 1 November 1992
PDF: 4 pages
Opt. Eng. 31(11) doi: 10.1117/12.60040
Published in: Optical Engineering Volume 31, Issue 11
Show Author Affiliations
Steven J. Schiff, Children's National Medical Center (United States)

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