國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Singular spectrum analysis for time ...

Golyandina, Nina.

Singular spectrum analysis for time series

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Singular spectrum analysis for time seriesby Nina Golyandina, Anatoly Zhigljavsky.
作者:	Golyandina, Nina.
其他作者:	Zhigljavsky, Anatoly.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg :2020.
面頁冊數:	ix, 146 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Time-series analysis.
電子資源:	https://doi.org/10.1007/978-3-662-62436-4
ISBN:	9783662624364$q(electronic bk.)

Singular spectrum analysis for time series
Golyandina, Nina.

Singular spectrum analysis for time series[electronic resource] /by Nina Golyandina, Anatoly Zhigljavsky. - Second edition. - Berlin, Heidelberg :Springer Berlin Heidelberg :2020. - ix, 146 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

1 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.

This 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.

ISBN: 9783662624364$q(electronic bk.)

Standard No.: 10.1007/978-3-662-62436-4doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280 / .G65 2020

Dewey Class. No.: 519.55

Singular spectrum analysis for time series
LDR:03525nmm a2200349 a 4500 001 589655
003 DE-He213
005 20210309152505.0
006 m d
007 cr nn 008maaau
008 210601s2020 gw s 0 eng d
020 $a 9783662624364$q(electronic bk.)
020 $a 9783662624357$q(paper)
024 7 $a 10.1007/978-3-662-62436-4 $2 doi
035 $a 978-3-662-62436-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA280 $b .G65 2020
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.55 $2 23
090 $a QA280 $b .G629 2020
100 1 $a Golyandina, Nina. $3 819204
245 1 0 $a Singular spectrum analysis for time series $h [electronic resource] / $c by Nina Golyandina, Anatoly Zhigljavsky.
250 $a Second edition.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2020.
300 $a ix, 146 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, $x 2191-544X
505 0 $a 1 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.
520 $a This 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.
650 0 $a Time-series analysis. $3 181890
650 0 $a Time-series analysis $x Mathematical models. $3 233807
650 0 $a Spectrum analysis. $3 188665
650 0 $a Biometry. $3 182039
650 0 $a Image processing. $3 182627
650 0 $a Signal processing. $3 182382
650 0 $a Speech processing systems. $3 184755
650 0 $a Statistics. $3 182057
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 825914
650 2 4 $a Biostatistics. $3 339693
700 1 $a Zhigljavsky, Anatoly. $3 274803
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in statistics. $3 557771
856 4 0 $u https://doi.org/10.1007/978-3-662-62436-4
950 $a Mathematics and Statistics (SpringerNature-11649)