000			03363nam a22004335i 4500
001			978-3-642-34913-3
003			DE-He213
005			20160302171209.0
007			cr nn 008mamaa
008			130125s2013 gw | s |||| 0|eng d
020			_a9783642349133 _9978-3-642-34913-3
024	7		_a10.1007/978-3-642-34913-3 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aGolyandina, Nina. _eauthor.
245	1	0	_aSingular Spectrum Analysis for Time Series _h[electronic resource] / _cby Nina Golyandina, Anatoly Zhigljavsky.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aIX, 120 p. 41 illus., 38 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Statistics, _x2191-544X
505	0		_aIntroduction: Preliminaries -- SSA Methodology and the Structure of the Book -- SSA Topics Outside the Scope of this Book -- Common Symbols and Acronyms -- Basic SSA: The Main Algorithm -- Potential of Basic SSA -- Models of Time Series and SSA Objectives -- Choice of Parameters in Basic SSA -- Some Variations of Basic SSA -- SSA for Forecasting, interpolation, Filtration and Estimation: SSA Forecasting Algorithms -- LRR and Associated Characteristic Polynomials -- Recurrent Forecasting as Approximate Continuation -- Confidence Bounds for the Forecast -- Summary and Recommendations on Forecasting Parameters -- Case Study: ‘Fortified Wine’ -- Missing Value Imputation -- Subspace-Based Methods and Estimation of Signal Parameters -- SSA and Filters.
520			_aSingular 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.
650		0	_aStatistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
700	1		_aZhigljavsky, Anatoly. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642349126
830		0	_aSpringerBriefs in Statistics, _x2191-544X
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-34913-3
912			_aZDB-2-SMA
999			_c199738 _d199738