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Singular Spectrum Analysis for Time SeriesBasic SSA

Singular Spectrum Analysis for Time Series: Basic SSA [In Chap. 2, SSA is normally considered as a model-free technique. The main body of Chap. 2 is devoted to careful description of Basic SSA, its main capabilities, choice of parameters and various indicators which help in recognizing good separability between different components and hence successful SSA decompositions. Several approaches for improving separability are examined; these approaches include rotations in chosen eigenspaces, the use of different matrix norms, as well as the use of prior and posterior information. Chapter 2 concludes with a description of multidimensional and multivariate extensions of SSA, which are applied to collections of time series and digital images respectively.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Singular Spectrum Analysis for Time SeriesBasic SSA

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Publisher
Springer Berlin Heidelberg
Copyright
© The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2020
ISBN
978-3-662-62435-7
Pages
21 –90
DOI
10.1007/978-3-662-62436-4_2
Publisher site
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Abstract

[In Chap. 2, SSA is normally considered as a model-free technique. The main body of Chap. 2 is devoted to careful description of Basic SSA, its main capabilities, choice of parameters and various indicators which help in recognizing good separability between different components and hence successful SSA decompositions. Several approaches for improving separability are examined; these approaches include rotations in chosen eigenspaces, the use of different matrix norms, as well as the use of prior and posterior information. Chapter 2 concludes with a description of multidimensional and multivariate extensions of SSA, which are applied to collections of time series and digital images respectively.]

Published: Nov 24, 2020

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