1 research outputs found
On the use of Singular Spectrum Analysis
Singular Spectrum Analysis (SSA) or Singular Value Decomposition (SVD) are
often used to de-noise univariate time series or to study their spectral
profile. Both techniques rely on the eigendecomposition of the cor- relation
matrix estimated after embedding the signal into its delayed coordi- nates. In
this work we show that the eigenvectors can be used to calculate the
coefficients of a set of filters which form a filter bank. The properties of
these filters are derived. In particular we show that their outputs can be
grouped according to their frequency response. Furthermore, the fre- quency at
the maximum of each frequency response and the corresponding eigenvalue can
provide a power spectrum estimation of the time series. Two different
applications illustrate how both characteristics can be applied to analyze
wideband signals in order to achieve narrow-band signals or to infer their
frequency occupation.Comment: 23 pages, 11 figure