Overview
- Presents the methodology of Singular Spectrum Analysis (SSA)
- Describes Multivariate Singular Spectrum Analysis (MSSA) and SSA for image processing (2D-SSA)
- Illustrated with examples and case studies
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
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Table of contents (3 chapters)
Keywords
About this book
Authors and Affiliations
About the authors
Anatoly Zhigljavsky has received his BSc, MSc and PhD degrees in mathematics and statistics at Faculty of Mathematics, St.Petersburg State University. He became professor of statistics at the St.Petersburg StateUniversity in 1989. Since 1997 he is a professor, Chair in Statistics at Cardiff University. Anatoly Zhigljavsky is the author or co-author of 10 monographs on the topics of time series analysis, stochastic global optimization, optimal experimental design and dynamical systems; he is the editor/co-editor of 9 books on various topics and the author of more than 150 research papers in refereed journals. He has organized several major conferences on time series analysis, experimental design and global optimization. In 2019, he has received a prestigious Constantine Caratheodory award by the International Society for Global Optimization for his contribution to stochastic optimization.
Bibliographic Information
Book Title: Singular Spectrum Analysis for Time Series
Authors: Nina Golyandina, Anatoly Zhigljavsky
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-662-62436-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2020
Softcover ISBN: 978-3-662-62435-7Published: 24 November 2020
eBook ISBN: 978-3-662-62436-4Published: 23 November 2020
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 2
Number of Pages: IX, 146
Number of Illustrations: 6 b/w illustrations, 38 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Signal, Image and Speech Processing, Statistics for Business, Management, Economics, Finance, Insurance, Biostatistics