Resolving time-series structure with a controlled wavelet transform

Steven J. Schiff

doi:10.1117/12.60040

1 November 1992 Resolving time-series structure with a controlled wavelet transform

Steven J. Schiff

Author Affiliations +

Optical Engineering, Vol. 31, Issue 11, (November 1992). https://doi.org/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.

Citation Download Citation

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

Published: 1 November 1992

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available