Skip to main content
SpringerLink
Log in
Book cover

Singular Spectrum Analysis for Time Series

  • Book
  • © 2013

Overview

Authors:
  1. Nina Golyandina

    1. , Mathematics, St. Petersburg University, St. Petersburg, Russia

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Anatoly Zhigljavsky

    1. School of Mathematics, Cardiff University, Cardiff, United Kingdom

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Presents the methodology of SSA
  • Shows how to use SSA both safely and with maximum effect
  • For professional statisticians, econometricians and specialists in any discipline
  • For students taking courses on applied time series analysis
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (3 chapters)

  1. Front Matter

    Pages i-vii
    Download chapter PDF

  2. Introduction

    • Nina Golyandina, Anatoly Zhigljavsky
    Pages 1-10

  3. Basic SSA

    • Nina Golyandina, Anatoly Zhigljavsky
    Pages 11-70

  4. SSA for Forecasting, Interpolation, Filtration and Estimation

    • Nina Golyandina, Anatoly Zhigljavsky
    Pages 71-119
Back to top

Keywords

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.

Reviews

From the reviews:

“This book is fully devoted to the methodology of a technique for time series analysis and forecasting called singular spectrum analysis (SSA). … The authors of the book are well-known statisticians, and specialists in time series analysis. … this book is a valuable addition to the literature on time series analysis and will therefore be well received by statisticians and specialists in many other fields interested in the analysis of time series data.” (K. S. Padmanabhan, zbMATH, Vol. 1276, 2014)

Authors and Affiliations

  • , Mathematics, St. Petersburg University, St. Petersburg, Russia

    Nina Golyandina

  • School of Mathematics, Cardiff University, Cardiff, United Kingdom

    Anatoly Zhigljavsky

Bibliographic Information

  • Book Title: Singular Spectrum Analysis for Time Series

  • Authors: Nina Golyandina, Anatoly Zhigljavsky

  • Series Title: SpringerBriefs in Statistics

  • DOI: https://doi.org/10.1007/978-3-642-34913-3

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Author(s) 2013

  • eBook ISBN: 978-3-642-34913-3Published: 19 January 2013

  • Series ISSN: 2191-544X

  • Series E-ISSN: 2191-5458

  • Edition Number: 1

  • Number of Pages: IX, 120

  • Number of Illustrations: 3 b/w illustrations, 38 illustrations in colour

  • Topics: Statistical Theory and Methods

Publish with us

Policies and ethics

Back to top

Search

Navigation