東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Singular spectrum analysis for time ...

Golyandina, Nina.

FindBook

Google Book

Amazon

博客來

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 series/ by 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.
內容註:	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.
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

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

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

532530
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 2257171
003 DE-He213
005 20210309152505.0
006 m d
007 cr nn 008maaau
008 220420s2020 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 3528129
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 532530
650 0 $a Time-series analysis $x Mathematical models. $3 674421
650 0 $a Spectrum analysis. $3 520440
650 0 $a Biometry. $3 531975
650 0 $a Image processing. $3 621117
650 0 $a Signal processing. $3 533904
650 0 $a Speech processing systems. $3 590570
650 0 $a Statistics. $3 517247
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
650 2 4 $a Biostatistics. $3 1002712
700 1 $a Zhigljavsky, Anatoly. $3 899061
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in statistics. $3 1565658
856 4 0 $u https://doi.org/10.1007/978-3-662-62436-4
950 $a Mathematics and Statistics (SpringerNature-11649)