LEADER 03693nam 22006015 450 001 9910438140003321 005 20250402124712.0 010 $a1-299-19783-3 010 $a3-642-34913-7 024 7 $a10.1007/978-3-642-34913-3 035 $a(CKB)2670000000328015 035 $a(EBL)1082855 035 $a(OCoLC)827212392 035 $a(SSID)ssj0000879715 035 $a(PQKBManifestationID)11495309 035 $a(PQKBTitleCode)TC0000879715 035 $a(PQKBWorkID)10853773 035 $a(PQKB)11174251 035 $a(DE-He213)978-3-642-34913-3 035 $a(MiAaPQ)EBC1082855 035 $a(PPN)168327716 035 $a(EXLCZ)992670000000328015 100 $a20130125d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSingular Spectrum Analysis for Time Series /$fby Nina Golyandina, Anatoly Zhigljavsky 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (125 p.) 225 1 $aSpringerBriefs in Statistics,$x2191-5458 300 $aDescription based upon print version of record. 311 08$a3-642-34912-9 320 $aIncludes bibliographical references. 327 $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. 330 $aSingular spectrum analysis (SSA) is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components and noise. It is based on the singular value decomposition of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity are assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability. The present book is devoted to the methodology of SSA and shows how to use SSA both safely and with maximum effect. Potential readers of the book include: professional statisticians and econometricians, specialists in any discipline in which problems of time series analysis and forecasting occur, specialists in signal processing and those needed to extract signals from noisy data, and students taking courses on applied time series analysis. 410 0$aSpringerBriefs in Statistics,$x2191-5458 606 $aStatistics 606 $aStatistical Theory and Methods 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 676 $a330.1951 700 $aGolyandina$b Nina$0145508 701 $aZhigljavsky$b Anatoly$0468354 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438140003321 996 $aSingular spectrum analysis for time series$92983594 997 $aUNINA