About this book
This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. 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, most notably those associated with time series and digital images. An effective, comfortable and accessible implementation of SSA is provided by the R-package Rssa, which is available from CRAN and reviewed in this book.
Written by prominent statisticians who have extensive experience with SSA, the book (a) presents the up-to-date SSA methodology, including multidimensional extensions, in language accessible to a large circle of users, (b) combines different versions of SSA into a single tool, (c) shows the diverse tasks that SSA can be used for, (d) formally describes the main SSA methods and algorithms, and (e) provides tutorials on the Rssa package and the use of 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 book is written on a level accessible to a broad audience and includes a wealth of examples; hence it can also be used as a textbook for undergraduate and postgraduate courses on time series analysis and signal processing.
- Book Title Singular Spectrum Analysis with R
- Series Title Use R!
- Series Abbreviated Title Use R
- DOI https://doi.org/10.1007/978-3-662-57380-8
- Copyright Information Springer-Verlag GmbH Germany, part of Springer Nature 2018
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Softcover ISBN 978-3-662-57378-5
- eBook ISBN 978-3-662-57380-8
- Series ISSN 2197-5736
- Series E-ISSN 2197-5744
- Edition Number 1
- Number of Pages XIII, 272
- Number of Illustrations 15 b/w illustrations, 106 illustrations in colour
Statistical Theory and Methods
Computer Imaging, Vision, Pattern Recognition and Graphics
Statistics for Business, Management, Economics, Finance, Insurance
Statistics and Computing/Statistics Programs
Statistics for Life Sciences, Medicine, Health Sciences
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