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Singular Spectrum Analysis with R

  • Nina Golyandina
  • Anton Korobeynikov
  • Anatoly Zhigljavsky
Book

Part of the Use R! book series (USE R)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
    Pages 1-30
  3. Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
    Pages 31-120
  4. Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
    Pages 121-188
  5. Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
    Pages 189-229
  6. Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
    Pages 231-270
  7. Back Matter
    Pages 271-272

About this book

Introduction

This 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.

Keywords

37M10, 68U10 forecasting signal processing singular spectrum analysis singular value decomposition time series image processing

Authors and affiliations

  • Nina Golyandina
    • 1
  • Anton Korobeynikov
    • 2
  • Anatoly Zhigljavsky
    • 3
  1. 1.Faculty of Mathematics and MechanicsSaint Petersburg State UniversitySaint PetersburgRussia
  2. 2.Faculty of Mathematics and MechanicsSaint Petersburg State UniversitySaint PetersburgRussia
  3. 3.School of MathematicsCardiff UniversityCardiffUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-57380-8
  • Copyright Information Springer-Verlag GmbH Germany, part of Springer Nature 2018
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-662-57378-5
  • Online ISBN 978-3-662-57380-8
  • Series Print ISSN 2197-5736
  • Series Online ISSN 2197-5744
  • Buy this book on publisher's site