文章基本信息

标题：On signal and extraneous roots in singular spectrum analysis
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作者：Konstantin Usevich
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2010
卷号：3
期号：3
页码：281-295
DOI：10.4310/SII.2010.v3.n3.a3
出版社：International Press
摘要：In the present paper we study properties of roots of characteristic polynomials for the linear recurrent formulae (LRF) that govern time series. We also investigate how the values of these roots affect Singular Spectrum Analysis implications, in what concerns separation of components, SSA forecasting and related signal parameter estimation methods. The roots of the characteristic polynomial for an LRF comprise the signal roots, which determine the structure of the time series, and extraneous roots. We show how the separability of two time series can be characterized in terms of their signal roots. All possible cases of exact separability are enumerated. We also examine properties of extraneous roots of the LRF used in SSA forecasting algorithms, which is equivalent to the Min-Norm vector in subspace-based estimation methods. We apply recent theoretical results for orthogonal polynomials on the unit circle, which enable us to precisely describe the asymptotic distribution of extraneous roots relative to the position of the signal roots.
关键词：singular spectrum analysis; SSA; separability; linear recurrent formula; LRF; continuation; extraneous roots; min-norm; subspace methods; orthogonal polynomials on the unit circle