Singular Spectrum Analysis With R
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Singular Spectrum Analysis With R
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Author : Nina Golyandina
language : en
Publisher: Springer
Release Date : 2018-06-14
Singular Spectrum Analysis With R written by Nina Golyandina and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-14 with Mathematics categories.
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.
Singular Spectrum Analysis
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Author : Hossein Hassani
language : en
Publisher: Springer
Release Date : 2018-06-25
Singular Spectrum Analysis written by Hossein Hassani and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-25 with Business & Economics categories.
This book provides a broad introduction to computational aspects of Singular Spectrum Analysis (SSA) which is a non-parametric technique and requires no prior assumptions such as stationarity, normality or linearity of the series. This book is unique as it not only details the theoretical aspects underlying SSA, but also provides a comprehensive guide enabling the user to apply the theory in practice using the R software. Further, it provides the user with step- by- step coding and guidance for the practical application of the SSA technique to analyze their time series databases using R. The first two chapters present basic notions of univariate and multivariate SSA and their implementations in R environment. The next chapters discuss the applications of SSA to change point detection, missing-data imputation, smoothing and filtering. This book is appropriate for researchers, upper level students (masters level and beyond) and practitioners wishing to revive their knowledge of times series analysis or to quickly learn about the main mechanisms of SSA.
Singular Spectrum Analysis For Time Series
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Author : Nina Golyandina
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-19
Singular Spectrum Analysis For Time Series written by Nina Golyandina and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-19 with Mathematics categories.
Singular spectrum analysis (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 seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components and noise. It is based on the singular value decomposition of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity are assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability. The present book is devoted to the methodology of SSA and shows how to use SSA both safely and with maximum effect. Potential readers of the book include: professional statisticians and econometricians, specialists in any discipline in which problems of time series analysis and forecasting occur, specialists in signal processing and those needed to extract signals from noisy data, and students taking courses on applied time series analysis.
Vibration Based Techniques For Damage Detection And Localization In Engineering Structures
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Author : Ali Salehzadeh Nobari
language : en
Publisher: World Scientific
Release Date : 2018-05-04
Vibration Based Techniques For Damage Detection And Localization In Engineering Structures written by Ali Salehzadeh Nobari and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-04 with Technology & Engineering categories.
In the oil and gas industries, large companies are endeavoring to find and utilize efficient structural health monitoring methods in order to reduce maintenance costs and time. Through an examination of the vibration-based techniques, this title addresses theoretical, computational and experimental methods used within this trend.By providing comprehensive and up-to-date coverage of established and emerging processes, this book enables the reader to draw their own conclusions about the field of vibration-controlled damage detection in comparison with other available techniques. The chapters offer a balance between laboratory and practical applications, in addition to detailed case studies, strengths and weakness are drawn from a broad spectrum of information.
Bayesian Spectrum Analysis And Parameter Estimation
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Author : G. Larry Bretthorst
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Bayesian Spectrum Analysis And Parameter Estimation written by G. Larry Bretthorst and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
This work is essentially an extensive revision of my Ph.D. dissertation, [1J. It 1S primarily a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently, we have included a great deal of introductory and tutorial material. Any person with the equivalent of the mathematics background required for the graduate level study of physics should be able to follow the material contained in this book, though not without eIfort. From the time the dissertation was written until now (approximately one year) our understanding of the parameter estimation problem has changed extensively. We have tried to incorporate what we have learned into this book. I am indebted to a number of people who have aided me in preparing this docu ment: Dr. C. Ray Smith, Steve Finney, Juana Sunchez, Matthew Self, and Dr. Pat Gibbons who acted as readers and editors. In addition, I must extend my deepest thanks to Dr. Joseph Ackerman for his support during the time this manuscript was being prepared.
Stochastic Global Optimization
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Author : Anatoly Zhigljavsky
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-11-20
Stochastic Global Optimization written by Anatoly Zhigljavsky and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-20 with Mathematics categories.
This book aims to cover major methodological and theoretical developments in the ?eld of stochastic global optimization. This ?eld includes global random search and methods based on probabilistic assumptions about the objective function. We discuss the basic ideas lying behind the main algorithmic schemes, formulate the most essential algorithms and outline the ways of their theor- ical investigation. We try to be mathematically precise and sound but at the same time we do not often delve deep into the mathematical detail, referring instead to the corresponding literature. We often do not consider the most g- eral assumptions, preferring instead simplicity of arguments. For example, we only consider continuous ?nite dimensional optimization despite the fact that some of the methods can easily be modi?ed for discrete or in?nite-dimensional optimization problems. The authors’ interests and the availability of good surveys on particular topics have in uenced the choice of material in the book. For example, there are excellent surveys on simulated annealing (both on theoretical and - plementation aspects of this method) and evolutionary algorithms (including genetic algorithms). We thus devote much less attention to these topics than they merit, concentrating instead on the issues which are not that well d- umented in literature. We also spend more time discussing the most recent ideas which have been proposed in the last few years.
Automatic Autocorrelation And Spectral Analysis
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Author : Piet M. T. Broersen
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-20
Automatic Autocorrelation And Spectral Analysis written by Piet M. T. Broersen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-20 with Computers categories.
"Automatic Autocorrelation and Spectral Analysis" gives random data a language to communicate the information they contain objectively. It takes advantage of greater computing power and robust algorithms to produce enough candidate models of a given group of data to be sure of providing a suitable one. Improved order selection guarantees that one of the best (often the best) will be selected automatically. Written for graduate signal processing students and for researchers and engineers using time series analysis for applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers: - tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models; - extensive support for the MATLAB® ARMAsel toolbox; - applications showing the methods in action; - appropriate mathematics for students to apply the methods with references for those who wish to develop them further.
Spectral Algorithms
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Author : Ravindran Kannan
language : en
Publisher: Now Publishers Inc
Release Date : 2009
Spectral Algorithms written by Ravindran Kannan and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
Spectral methods refer to the use of eigenvalues, eigenvectors, singular values and singular vectors. They are widely used in Engineering, Applied Mathematics and Statistics. More recently, spectral methods have found numerous applications in Computer Science to "discrete" as well "continuous" problems. Spectral Algorithms describes modern applications of spectral methods, and novel algorithms for estimating spectral parameters. The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations. A feature of many modern applications is the massive amount of input data. While sophisticated algorithms for matrix computations have been developed over a century, a more recent development is algorithms based on "sampling on the y" from massive matrices. Good estimates of singular values and low rank approximations of the whole matrix can be provably derived from a sample. The main emphasis in the second part of the book is to present these sampling methods with rigorous error bounds. It also presents recent extensions of spectral methods from matrices to tensors and their applications to some combinatorial optimization problems.
Matrix Methods
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Author : Vadim Olshevsky
language : en
Publisher: World Scientific
Release Date : 2010
Matrix Methods written by Vadim Olshevsky and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematics categories.
Matrix methods provide the key to many problems in pure and applied mathematics. However, linear algebra theory, numerical algorithms and matrices in FEM/BEM applications usually live as if in three separate worlds. In this volume, maybe for the first time ever, they are compiled together as one entity as it was at the Moscow meeting, where the algebraic part was impersonated by Hans Schneider, algorithms by Gene Golub, and applications by Guri Marchuk. All topics intervened in plenary sessions are specially categorized into three sections of this volume. --