Spectral Analysis For Univariate Time Series
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Spectral Analysis For Univariate Time Series
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Author : Donald B. Percival
language : en
Publisher: Cambridge University Press
Release Date : 2020-03-19
Spectral Analysis For Univariate Time Series written by Donald B. Percival and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-19 with Mathematics categories.
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.
The Spectral Analysis Of Time Series
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Author : Lambert Herman Koopmans
language : en
Publisher:
Release Date : 1974
The Spectral Analysis Of Time Series written by Lambert Herman Koopmans and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1974 with Mathematics categories.
The Spectral Analysis of Time Series ...
Spectral Analysis Of Time Series Data
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Author : Rebecca M. Warner
language : en
Publisher: Guilford Press
Release Date : 1998-05-22
Spectral Analysis Of Time Series Data written by Rebecca M. Warner and has been published by Guilford Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-05-22 with Social Science categories.
This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.
Spectral Analysis And Time Series Univariate Series
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Author : Maurice Bertram Priestley
language : en
Publisher:
Release Date : 1981
Spectral Analysis And Time Series Univariate Series written by Maurice Bertram Priestley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with Mathematics categories.
Time Series Analysis
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Author : William W. S. Wei
language : en
Publisher: Addison-Wesley Longman
Release Date : 2006
Time Series Analysis written by William W. S. Wei and has been published by Addison-Wesley Longman this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Time-series analysis categories.
With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Overview. Fundamental Concepts. Stationary Time Series Models. Nonstationary Time Series Models. Forecasting. Model Identification. Parameter Estimation, Diagnostic Checking, and Model Selection. Seasonal Time Series Models. Testing for a Unit Root. Intervention Analysis and Outlier Detection. Fourier Analysis. Spectral Theory of Stationary Processes. Estimation of the Spectrum. Transfer Function Models. Time Series Regression and GARCH Models. Vector Time Series Models. More on Vector Time Series. State Space Models and the Kalman Filter. Long Memory and Nonlinear Processes. Aggregation and Systematic Sampling in Time Series. For all readers interested in time series analysis.
The Spectral Analysis Of Time Series
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Author : L. H. Koopmans
language : en
Publisher: Academic Press
Release Date : 2014-05-12
The Spectral Analysis Of Time Series written by L. H. Koopmans and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Mathematics categories.
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.
The Spectral Analysis Of Time Series
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Author : I. G. Žurbenko
language : en
Publisher: North Holland
Release Date : 1986
The Spectral Analysis Of Time Series written by I. G. Žurbenko and has been published by North Holland this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Spectral theory (Mathematics) categories.
Examined in this volume are the asymptotic properties of spectral estimates of stationary processes and random fields. A new class of lag window estimates indifferent to remote frequencies is introduced and pseudorandom sequences are investigated from the point of view of their nearness to the sequence of white noise. Principles and algorithms are given for constructing an ideal sequence. A good achievement is the new estimates of higher spectral density asymptotically unbiased and consistent for all admissible values of the argument. A new type of the random number generator which is sufficiently close to white noise is introduced.
The Analysis Of Time Series
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Author : Christopher Chatfield
language : en
Publisher: Chapman & Hall
Release Date : 1984
The Analysis Of Time Series written by Christopher Chatfield and has been published by Chapman & Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Mathematics categories.
Simple descriptive techniques; Probability models for time series; Estimation in the domain; Forecasting; Stationary processes in the frequency domain; Spectral analysis; Bivariate processes; Linear systems.
Multivariate Time Series Analysis And Applications
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Author : William W. S. Wei
language : en
Publisher: John Wiley & Sons
Release Date : 2019-03-18
Multivariate Time Series Analysis And Applications written by William W. S. Wei and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-18 with Mathematics categories.
An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.
Singular Spectrum Analysis For Time Series
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Author : Nina Golyandina
language : en
Publisher: Springer Nature
Release Date : 2020-11-23
Singular Spectrum Analysis For Time Series written by Nina Golyandina and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-23 with Mathematics categories.
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.