Download or read book Time Series with Mixed Spectra written by Ta-Hsin Li and published by CRC Press. This book was released on 2016-04-19 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject. Filling this void, Time Series with Mixed Spectra focuses on the methods and theory for the stati

Download or read book Semiparametric Bayesian Inference for Time Series with Mixed Spectra written by Christopher K. Carter and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Bayesian analysis is presented of a time series which is the sum of a stationary component with a smooth spectral density and a deterministic component consisting of a linear combination of a trend and periodic terms. The periodic terms may have known or unknown frequencies. The advantage of our approach is that different features of the data such as the regression parameters, the spectral density, unknown frequencies, and missing observations are combined in a hierarchical Bayesian framework and estimated simultaneously. A Bayesian test to detect the presence of deterministic components in the data is also constructed. By using an asymptotic approximation to the likelihood, the computation is carried out efficiently using Markov chain Monte Carlo in O(Mn) operations, where n is the sample size and M and is the number of iterations. We show empirically that our approach works well on real and simulated examples.

Download or read book A New Approach to Time Series with Mixed Spectra written by George Ronald Hext and published by . This book was released on 1966 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: The time series considered have jumps in their spectral distribution function; that is, the series is the sum of a 'signal' component, comprising a finite linear sum of pure sine-waves, and a 'noise' component, having continuous spectral density function. Given a set of observations from such a time series the primary problem is to estimate the 'signal' frequencies, the power in each component of the signal, and the 'noise' spectral density at these frequencies. The essence of the method used is as follows. For a given set of observations from such a series, and for each frequency that might yield a signal component, several estimates of the spectral density are made, using spectral windows of different bandwidths. To a first approximation, the noise component of the estimate is the same for every window, while the part of the estimate due to the signal is inversely proportional to the bandwidth of the window. Thus using a regression technique, one can separate the signal power from the noise spectral density at the given frequency and estimate these two quantities. These ideas are developed as follows. After a historical introduction, the early part of the thesis is devoted to the 'probability' aspects of the problem. First some results are proved that apply to the 'noise' series or any stationary time series. They give extensions and refinements of early approximations for the expected value of the spectral estimate, and for the covariance between two spectral estimates; these include the rates at which the limiting values are attained.

Download or read book The Spectral Analysis of Time Series written by Lambert Herman Koopmans and published by . This book was released on 1974 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Spectral Analysis of Time Series ...

Download or read book The Spectral Analysis of Time Series written by L. H. Koopmans and published by Academic Press. This book was released on 2014-05-12 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Nonstationarities in Hydrologic and Environmental Time Series written by A.R. Rao and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e . the value of the signal at each instant in time is well defined . However, the time representation of a signal is poorly localized in frequency , i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain . On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time. In studying stationary or conditionally stationary processes with mixed spectra , the separate use of time domain and frequency domain analyses is sufficient to reveal the structure of the process . Results discussed in the previous chapters suggest that the time series analyzed in this book are conditionally stationary processes with mixed spectra. Additionally, there is some indication of nonstationarity, especially in longer time series.

Download or read book Spectral Analysis for Univariate Time Series written by Donald B. Percival and published by Cambridge University Press. This book was released on 2020-03-19 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Time Series Analysis and Cyclostratigraphy written by Graham P. Weedon and published by Cambridge University Press. This book was released on 2005-09-15 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasingly environmental scientists, palaeoceanographers and geologists are collecting quantitative records of environmental changes (time-series) from sediments, ice cores, cave calcite, corals and trees. This book explains how to analyse these records, using straightforward explanations and diagrams rather than formal mathematical derivations. All the main cyclostratigraphic methods are covered including spectral analysis, cross-spectral analysis, filtering, complex demodulation, wavelet and singular spectrum analysis. Practical problems of time-series analysis, including those of distortions of environmental signals during stratigraphic encoding, are considered in detail. Recent research into various types of tidal and climatic cycles is summarised. The book ends with an extensive reference section, and an appendix listing sources of computer algorithms. This book provides the ideal reference for all those using time-series analysis to study the nature and history of climatic and tidal cycles. It is suitable for senior undergraduate and graduate courses in environmental science, palaeoceanography and geology.

Download or read book Semiparametric Bayesian Inference for Time Series with Mixed Spectra written by C. K. Carter and published by . This book was released on 1995 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Time Series Analysis Papers written by Emanuel Parzen and published by . This book was released on 1967 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: On consistent estimates of the spectral density of a stationary time series; Analysis of a general system for the detection of amplitude-modulated noise; A central limit theorem for multilinear stochastic processes; Conditions that a stochastic process ber egodic; On consistent estimates of the spectrum of a stationary time series; On choosing an estimate of the spectral density function of a stationary time series; On asymptotically efficient consistent estimates of the spectral density function of a stationary time series; General considerations in the analysis of spectra; Mathematical considerations in the estimation of spectra; Spectral analysis of asymptotically stationary time series; On spectral analysis with missing observations and amplitude modulation; Notes on fourier analysis and spectral windows; Statistical inference on time series by Hilbert space methods; An approach to time series analysis; Regression analysis of continuous parameter time series; A new approach to the synthesis of optimal smoothing and prediction systems; Probability density functionals and reproducing kernel hilbert spaces; Extraction and detection problems and reproducing kernel hilbert spaces; On estimation of a probability density function and mode; On models for the probability of fatigue failure of a structure; An approach to empirical time series analysis.

Download or read book Developments in Time Series Analysis written by T. Subba Rao and published by CRC Press. This book was released on 1993-07-01 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains 27 papers, written by time series analysts, dealing with statistical theory, methodology and applications. The emphasis is on the recent developments in the analysis of linear, onlinear (non-Gaussian), stationary and nonstationary time series. The topics include cointegration, estimation and asymptotic theory, Kalman filtering, nonparametric statistical inference, long memory models, nonlinear models, spectral analysis of stationary and nonstationary processes. Quite a number of papers are devoted to modelling and analysis of real time series, and the econometricians, mathematical statisticians, communications engineers and scientists who use time series techniques and Fourier analysis should find the papers in this volume useful.

Download or read book Spectral Analysis and Parametric Methods for Seasonal Adjustment of Economic Time Series written by Harry Myer Rosenblatt and published by . This book was released on 1965 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability written by Lucien Marie Le Cam and published by Univ of California Press. This book was released on 1967 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Singular Spectrum Analysis for Time Series written by Nina Golyandina and published by Springer Science & Business Media. This book was released on 2013-01-19 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Univariate Time Series in Geosciences written by Hans Gilgen and published by Springer Science & Business Media. This book was released on 2006-01-16 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a detailed introduction to the statistical analysis of geophysical time series, using numerous examples and exercises to build proficiency. The exercises lead the reader to explore the meaning of concepts such as the estimation of the linear time series (AMRA) models or spectra. The book also serves as a guide to using the open-source "R" program for statistical analysis of time series.

Download or read book Time Series Analysis Univariate and Multivariate Methods written by William W. S. Wei and published by Pearson. This book was released on 2018-03-14 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.

Download or read book Recent Econometric Techniques for Macroeconomic and Financial Data written by Gilles Dufrénot and published by Springer Nature. This book was released on 2020-11-21 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.