TY - JOUR

T1 - Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing

AU - Golyandina, Nina

N1 - Publisher Copyright: © 2020 Wiley Periodicals, Inc.

PY - 2020/7/1

Y1 - 2020/7/1

N2 - Singular spectrum analysis (SSA), starting from the second half of the 20th century, has been a rapidly developing method of time series analysis. Since it can be called principal component analysis (PCA) for time series, SSA will definitely be a standard method in time series analysis and signal processing in the future. Moreover, the problems solved by SSA are considerably wider than that for PCA. In particular, the problems of frequency estimation, forecasting and missing values imputation can be solved within the framework of SSA. The idea of SSA came from different scientific communities, such as that of researchers in time series analysis (Karhunen-Loeve decomposition), signal processing (low-rank approximation and frequency estimation) and multivariate data analysis (PCA). Also, depending on the area of applications, different viewpoints on the same algorithms, choice of parameters, and methodology as a whole are considered. Thus, the aim of the paper is to describe and compare different viewpoints on SSA and its modifications and extensions to give people from different scientific communities the possibility to be aware of potentially new aspects of the method. This article is categorized under: Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

AB - Singular spectrum analysis (SSA), starting from the second half of the 20th century, has been a rapidly developing method of time series analysis. Since it can be called principal component analysis (PCA) for time series, SSA will definitely be a standard method in time series analysis and signal processing in the future. Moreover, the problems solved by SSA are considerably wider than that for PCA. In particular, the problems of frequency estimation, forecasting and missing values imputation can be solved within the framework of SSA. The idea of SSA came from different scientific communities, such as that of researchers in time series analysis (Karhunen-Loeve decomposition), signal processing (low-rank approximation and frequency estimation) and multivariate data analysis (PCA). Also, depending on the area of applications, different viewpoints on the same algorithms, choice of parameters, and methodology as a whole are considered. Thus, the aim of the paper is to describe and compare different viewpoints on SSA and its modifications and extensions to give people from different scientific communities the possibility to be aware of potentially new aspects of the method. This article is categorized under: Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

KW - DYNAMICS

KW - ENHANCEMENT

KW - IMPROVEMENT

KW - MATRIX

KW - MULTIVARIATE

KW - NOISE

KW - OSCILLATIONS

KW - PARAMETERS

KW - SEPARABILITY

KW - VALUE DECOMPOSITION

KW - decomposition

KW - forecasting

KW - signal processing

KW - singular spectrum analysis

KW - time series

UR - http://www.scopus.com/inward/record.url?scp=85078628584&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/67795d73-2b7e-3deb-a022-4dd2fd4865fe/

U2 - 10.1002/wics.1487

DO - 10.1002/wics.1487

M3 - Review article

AN - SCOPUS:85078628584

VL - 12

JO - Wiley Interdisciplinary Reviews: Computational Statistics

JF - Wiley Interdisciplinary Reviews: Computational Statistics

SN - 1939-5108

IS - 4

M1 - e1487

ER -