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

May 1992

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1992spie.1637..151s&link_type=abstract

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

Astronomy and Astrophysics

Astronomy

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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