Singular Spectrum Analysis for Time Series (Paperback, 2013 ed.)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	SpringerBriefs in Statistics
Release date:	2013
First published:	2013
Authors:	Nina Golyandina • Anatoly Zhigljavsky
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	120
Edition:	2013 ed.
ISBN-13:	978-3-642-34912-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Promotions
LSN:	3-642-34912-9
Barcode:	9783642349126

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