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Singular Spectrum Analysis - A New Tool in Time Series Analysis (Paperback, Softcover reprint of hardcover 1st ed. 1996)
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Singular Spectrum Analysis - A New Tool in Time Series Analysis (Paperback, Softcover reprint of hardcover 1st ed. 1996)
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The term singular spectrum comes from the spectral (eigenvalue)
decomposition of a matrix A into its set (spectrum) of eigenvalues.
These eigenvalues, A, are the numbers that make the matrix A -AI
singular. The term singular spectrum analysis* is unfortunate since
the traditional eigenvalue decomposition involving multivariate
data is also an analysis of the singular spectrum. More properly,
singular spectrum analysis (SSA) should be called the analysis of
time series using the singular spectrum. Spectral decomposition of
matrices is fundamental to much the ory of linear algebra and it
has many applications to problems in the natural and related
sciences. Its widespread use as a tool for time series analysis is
fairly recent, however, emerging to a large extent from
applications of dynamical systems theory (sometimes called chaos
theory). SSA was introduced into chaos theory by Fraedrich (1986)
and Broomhead and King (l986a). Prior to this, SSA was used in
biological oceanography by Colebrook (1978). In the digi tal signal
processing community, the approach is also known as the
Karhunen-Loeve (K-L) expansion (Pike et aI., 1984). Like other
techniques based on spectral decomposition, SSA is attractive in
that it holds a promise for a reduction in the dimen- * Singular
spectrum analysis is sometimes called singular systems analysis or
singular spectrum approach. vii viii Preface sionality. This
reduction in dimensionality is often accompanied by a simpler
explanation of the underlying physics.
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