Resolving time-series structure with a controlled wavelet transform

Details Resolving time-series structure with a controlled wavelet transform Resolving time-series structure with a controlled wavelet transform

Nov 1992

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1992opten..31.2492s&link_type=abstract

Optical Engineering 31(11), 2492-2495, Brian J. Thompson; Ed.

Astronomy and Astrophysics

Astronomy

5

Scientific paper

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 straight-forward 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.

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