Singular spectrum analysis for time series

by Golyandina, Nina

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Title Statements

Full title :Singular spectrum analysis for time series / Nina Golyandina, Anatoly Zhigljavsky.

Summary

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.

Edition Statements

Publication Date:©2013.

Publication Statements

Publisher(s):Springer
Place of Publication:Berlin
Language:eng
Publication Date:©2013.

The Physical Object

Description:1 online resource

Edition Identifiers

ISBN:9783642349133, 3642349137, 9783662624364, 3662624362
DOI:10.1007/978-3-642-34913-3
oclc:ocn826708350
LC Classification:QA280
Dewey Decimal:519.5/5

Subjects

Mathematical statistics Série chronologique -- Modèles mathémat… Spectrum analysis Spektralanalyse Statistical Analysis Statistical Theory and Methods Statistics Statistics, general statistiek Statistiek (algemeen) Statistische Analyse Time-series analysis -- Mathematical mo… Zeitreihe