LEADER 03981oam 2200493 450 001 9910484289003321 005 20230619185508.0 010 $a3-662-62436-2 024 7 $a10.1007/978-3-662-62436-4 035 $a(CKB)4100000011610307 035 $a(MiAaPQ)EBC6407545 035 $a(DE-He213)978-3-662-62436-4 035 $a(PPN)252503090 035 $a(EXLCZ)994100000011610307 100 $a20210515d2020 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSingular spectrum analysis for time series /$fNina Golyandina, Anatoly Zhigljavsky 205 $aSecond edition. 210 1$aBerlin, Germany :$cSpringer,$d[2020] 210 4$dİ2020 215 $a1 online resource (IX, 146 p. 44 illus., 38 illus. in color.) 225 1 $aSpringerBriefs in Statistics,$x2191-544X 311 $a3-662-62435-4 327 $a1 Introduction -- 1.1 Overview of SSA methodology and the structure of the book -- 1.2 SSA and other techniques -- 1.3 Computer implementation of SSA -- 1.4 Historical and bibliographical remarks -- 1.5 Common symbols and acronyms -- 2 Basic SSA - 2.1 The main algorithm -- 2.2 Potential of Basic SSA -- 2.3 Models of time series and SSA objectives -- 2.4 Choice of parameters in Basic SSA -- 2.5 Some variations of Basic SSA -- 2.6 Multidimensional and multivariate extensions of SSA -- 3 SSA for forecasting, interpolation, filtering and estimation -- 3.1 SSA forecasting algorithms -- 3.2 LRR and associated characteristic polynomials -- 3.3 Recurrent forecasting as approximate continuation -- 3.4 Confidence bounds for the forecasts -- 3.5 Summary and recommendations on forecasting parameters -- 3.6 Case study: ?Fortified wine? -- 3.7 Imputation of missing values -- 3.8 Subspace-based methods and estimation of signal parameters -- 3.9 SSA and filters -- 3.10 Multidimensional/Multivariate SSA. 330 $aThis book gives an overview of singular spectrum analysis (SSA). SSA is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas. Rapidly increasing number of novel applications of SSA is a consequence of the new fundamental research on SSA and the recent progress in computing and software engineering which made it possible to use SSA for very complicated tasks that were unthinkable twenty years ago. In this book, the methodology of SSA is concisely but at the same time comprehensively explained by two prominent statisticians with huge experience in SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The second edition of the book contains many updates and some new material including a thorough discussion on the place of SSA among other methods and new sections on multivariate and multidimensional extensions of SSA. 410 0$aSpringerBriefs in Statistics,$x2191-544X 606 $aTime-series analysis$xMathematical models 606 $aSpectrum analysis 615 0$aTime-series analysis$xMathematical models. 615 0$aSpectrum analysis. 676 $a519.55 700 $aGolyandina$b Nina$0145508 702 $aZhigli?a?vskii?$b A. A$g(Anatolii? Aleksandrovich), 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910484289003321 996 $aSingular Spectrum Analysis for Time Series$92510999 997 $aUNINA