Singular Spectrum Analysis for Time Series

Name: Singular Spectrum Analysis for Time Series
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783642349126
Price: 1476 CZK
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Autor: Anatoly Zhigljavsky, Nina Golyandina

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

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Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting combining el...

Informace o knize

Autor

Anatoly Zhigljavsky, Nina Golyandina

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2013

Stránek

120

EAN

9783642349126

ISBN

3642349129

Enbook ID

01662642

Vydavatel

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Hmotnost

212

Rozměry

239 x 153 x 10

Kompletní popis

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.

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Singular Spectrum Analysis for Time Series

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