1 November 1992 Resolving time-series structure with a controlled wavelet transform
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Optical Engineering, 31(11), (1992). doi:10.1117/12.60040
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.
Steven J. Schiff, "Resolving time-series structure with a controlled wavelet transform," Optical Engineering 31(11), (1 November 1992). http://dx.doi.org/10.1117/12.60040
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KEYWORDS
Fourier transforms

Wavelet transforms

Wavelets

Chaos

Data modeling

Gaussian filters

Interference (communication)

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