000			03462nam a22003855i 4500
001			307228
003			MX-SnUAN
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007			cr nn 008mamaa
008			150903s2013 gw | o |||| 0|eng d
020			_a9783642349133 _99783642349133
024	7		_a10.1007/9783642349133 _2doi
035			_avtls000360608
039		9	_a201509030628 _bVLOAD _c201405070302 _dVLOAD _y201402201435 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aGolyandina, Nina. _eautor _9346763
245	1	0	_aSingular Spectrum Analysis for Time Series / _cby Nina Golyandina, Anatoly Zhigljavsky.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aIx, 120 páginas 41 ilustraciones, 38 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Statistics, _x2191-544X
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aZhigljavsky, Anatoly. _eautor _9303314
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783642349126
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-34913-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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