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000843514 019__ $$a1041513684$$a1041888078
000843514 020__ $$a9783662573808$$q(electronic book)
000843514 020__ $$a3662573806$$q(electronic book)
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000843514 0247_ $$a10.1007/978-3-662-57380-8$$2doi
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000843514 035__ $$aSP(OCoLC)1040612971$$z(OCoLC)1041513684$$z(OCoLC)1041888078
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000843514 050_4 $$aHA32
000843514 08204 $$a005.55$$223
000843514 1001_ $$aGolyandina, Nina,$$eauthor.
000843514 24510 $$aSingular spectrum analysis with R /$$cby Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky.
000843514 264_1 $$aBerlin, Heidelberg :$$bSpringer,$$c2018.
000843514 300__ $$a1 online resource.
000843514 336__ $$atext$$btxt$$2rdacontent
000843514 337__ $$acomputer$$bc$$2rdamedia
000843514 338__ $$aonline resource$$bcr$$2rdacarrier
000843514 347__ $$atext file$$bPDF$$2rda
000843514 4901_ $$aUse R!,$$x2197-5736
000843514 5050_ $$aPreface -- Common symbols and acronyms -- Contents -- 1 Introduction: Overview -- 2 SSA analysis of one-dimensional time series -- 3 Parameter estimation, forecasting, gap filling -- 4 SSA for multivariate time series -- 5 Image processing -- Index -- References.
000843514 506__ $$aAccess limited to authorized users.
000843514 520__ $$aThis comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas, most notably those associated with time series and digital images. An effective, comfortable and accessible implementation of SSA is provided by the R-package Rssa, which is available from CRAN and reviewed in this book. Written by prominent statisticians who have extensive experience with SSA, the book (a) presents the up-to-date SSA methodology, including multidimensional extensions, in language accessible to a large circle of users, (b) combines different versions of SSA into a single tool, (c) shows the diverse tasks that SSA can be used for, (d) formally describes the main SSA methods and algorithms, and (e) provides tutorials on the Rssa package and the use of SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The book is written on a level accessible to a broad audience and includes a wealth of examples; hence it can also be used as a textbook for undergraduate and postgraduate courses on time series analysis and signal processing.
000843514 650_0 $$aStatistics$$xComputer programs.
000843514 650_0 $$aSpectrum analysis$$xComputer programs.
000843514 7001_ $$aKorobeynikov, Anton,$$eauthor.
000843514 7001_ $$aZhigljavsky, Anatoly,$$eauthor.
000843514 77608 $$iPrint version: $$z3662573784$$z9783662573785$$w(OCoLC)1028942428
000843514 830_0 $$aUse R!
000843514 852__ $$bebk
000843514 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-662-57380-8$$zOnline Access$$91397441.1
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