Authors
Robert Vautard, Pascal Yiou, Michael Ghil
Publication date
1992/9/15
Journal
Physica D: Nonlinear Phenomena
Volume
58
Issue
1-4
Pages
95-126
Publisher
North-Holland
Description
Singular-spectrum analysis (SSA) is developed further, based on experience with applications to geophysical time series. It is shown that SSA provides a crude but robust approximation of strange attractors by tori, in the presence of noise. The method works well for short, noisy time series.
The lagged-covariance matrix of the processes studied is the basis of SSA. We select subsets of eigenelements and associated principal components (PCs) in order to provide (i) a noise-reduction algorithm, (ii) a detrending algorithm, and (iii) an algorithm for the identification of oscillatory components. Reconstructed components (RCs) are developed to provide optimal reconstruction of a dynamic process at precise epochs, rather than averaged over the window length of the analysis.
SSA is combined with advanced spectral-analysis methods - the maximum entropy method (MEM) and the multi-taper method (MTM) - to refine the …
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Scholar articles
R Vautard, P Yiou, M Ghil - Physica D: Nonlinear Phenomena, 1992