The Spectral Analysis of Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Spectral Analysis of Time Series write by L. H. Koopmans. This book was released on 2014-05-12. The Spectral Analysis of Time Series available in PDF, EPUB and Kindle. The Spectral Analysis of Time Series describes the techniques and theory of the frequency domain analysis of time series. The book discusses the physical processes and the basic features of models of time series. The central feature of all models is the existence of a spectrum by which the time series is decomposed into a linear combination of sines and cosines. The investigator can used Fourier decompositions or other kinds of spectrals in time series analysis. The text explains the Wiener theory of spectral analysis, the spectral representation for weakly stationary stochastic processes, and the real spectral representation. The book also discusses sampling, aliasing, discrete-time models, linear filters that have general properties with applications to continuous-time processes, and the applications of multivariate spectral models. The text describes finite parameter models, the distribution theory of spectral estimates with applications to statistical inference, as well as sampling properties of spectral estimates, experimental design, and spectral computations. The book is intended either as a textbook or for individual reading for one-semester or two-quarter course for students of time series analysis users. It is also suitable for mathematicians or professors of calculus, statistics, and advanced mathematics.
Singular Spectrum Analysis for Time Series
Singular Spectrum Analysis for Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Singular Spectrum Analysis for Time Series write by Nina Golyandina. This book was released on 2020-11-23. Singular Spectrum Analysis for Time Series available in PDF, EPUB and Kindle. 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.
Spectral Analysis for Univariate Time Series
Spectral Analysis for Univariate Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Spectral Analysis for Univariate Time Series write by Donald B. Percival. This book was released on 2020-03-19. Spectral Analysis for Univariate Time Series available in PDF, EPUB and Kindle. Spectral analysis is widely used to interpret time series collected in diverse areas. This book covers the statistical theory behind spectral analysis and provides data analysts with the tools needed to transition theory into practice. Actual time series from oceanography, metrology, atmospheric science and other areas are used in running examples throughout, to allow clear comparison of how the various methods address questions of interest. All major nonparametric and parametric spectral analysis techniques are discussed, with emphasis on the multitaper method, both in its original formulation involving Slepian tapers and in a popular alternative using sinusoidal tapers. The authors take a unified approach to quantifying the bandwidth of different nonparametric spectral estimates. An extensive set of exercises allows readers to test their understanding of theory and practical analysis. The time series used as examples and R language code for recreating the analyses of the series are available from the book's website.
Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series write by K. Dzhaparidze. This book was released on 1986. Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series available in PDF, EPUB and Kindle. . . ) (under the assumption that the spectral density exists). For this reason, a vast amount of periodical and monographic literature is devoted to the nonparametric statistical problem of estimating the function tJ( T) and especially that of leA) (see, for example, the books [4,21,22,26,56,77,137,139,140,]). However, the empirical value t;; of the spectral density I obtained by applying a certain statistical procedure to the observed values of the variables Xl' . . . , X , usually depends in n a complicated manner on the cyclic frequency). . This fact often presents difficulties in applying the obtained estimate t;; of the function I to the solution of specific problems rela ted to the process X . Theref ore, in practice, the t obtained values of the estimator t;; (or an estimator of the covariance function tJ~( T» are almost always "smoothed," i. e. , are approximated by values of a certain sufficiently simple function 1 = 1
The Spectral Analysis of Time Series
The Spectral Analysis of Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Spectral Analysis of Time Series write by Lambert H. Koopmans. This book was released on 1995-05-18. The Spectral Analysis of Time Series available in PDF, EPUB and Kindle. To tailor time series models to a particular physical problem and to follow the working of various techniques for processing and analyzing data, one must understand the basic theory of spectral (frequency domain) analysis of time series. This classic book provides an introduction to the techniques and theories of spectral analysis of time series. In a discursive style, and with minimal dependence on mathematics, the book presents the geometric structure of spectral analysis. This approach makes possible useful, intuitive interpretations of important time series parameters and provides a unified framework for an otherwise scattered collection of seemingly isolated results.The books strength lies in its applicability to the needs of readers from many disciplines with varying backgrounds in mathematics. It provides a solid foundation in spectral analysis for fields that include statistics, signal process engineering, economics, geophysics, physics, and geology. Appendices provide details and proofs for those who are advanced in math. Theories are followed by examples and applications over a wide range of topics such as meteorology, seismology, and telecommunications.Topics covered include Hilbert spaces; univariate models for spectral analysis; multivariate spectral models; sampling, aliasing, and discrete-time models; real-time filtering; digital filters; linear filters; distribution theory; sampling properties ofspectral estimates; and linear prediction. - Hilbert spaces - univariate models for spectral analysis - multivariate spectral models - sampling, aliasing, and discrete-time models - real-time filtering - digital filters - linear filters - distribution theory - sampling properties of spectral estimates - linear prediction
