Singular Spectrum Analysis for Time Series

  • Nina Golyandina
  • Anatoly Zhigljavsky

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Nina Golyandina, Anatoly Zhigljavsky
    Pages 1-10
  3. Nina Golyandina, Anatoly Zhigljavsky
    Pages 11-70
  4. Nina Golyandina, Anatoly Zhigljavsky
    Pages 71-119

About this book


Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components and noise. It is based on the singular value decomposition of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity are assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability. The present book is devoted to the methodology of SSA and shows how to use SSA both safely and with maximum effect. Potential readers of the book include: professional statisticians and econometricians, specialists in any discipline in which problems of time series analysis and forecasting occur, specialists in signal processing and those needed to extract signals from noisy data, and students taking courses on applied time series analysis.


data analysis forecasting signal processing singular value decomposition time series

Authors and affiliations

  • Nina Golyandina
    • 1
  • Anatoly Zhigljavsky
    • 2
  1. 1., MathematicsSt. Petersburg UniversitySt. PetersburgRussia
  2. 2.School of MathematicsCardiff UniversityCardiffUnited Kingdom

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-34912-6
  • Online ISBN 978-3-642-34913-3
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site