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Multivariate Time Series Analysis And Applications
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Book Synopsis Multivariate Time Series Analysis and Applications by : William W. S. Wei
Download or read book Multivariate Time Series Analysis and Applications written by William W. S. Wei and published by John Wiley & Sons. This book was released on 2019-03-18 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Multivariate Time Series Analysis by : Ruey S. Tsay
Download or read book Multivariate Time Series Analysis written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2013-11-11 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
Book Synopsis Elements of Multivariate Time Series Analysis by : Gregory C. Reinsel
Download or read book Elements of Multivariate Time Series Analysis written by Gregory C. Reinsel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years. Although the methods are rather well developed and understood for univarjate time series analysis, the situation is not so complete for the multivariate case. This book is designed to introduce the basic concepts and methods that are useful in the analysis and modeling of multivariate time series, with illustrations of these basic ideas. The development includes both traditional topics such as autocovariance and auto correlation matrices of stationary processes, properties of vector ARMA models, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and model checking diagnostics for residuals, as well as topics of more recent interest for vector ARMA models such as reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate unit-root models and cointegration structure, and state-space models and Kalman filtering techniques and applications. This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not considered here. For that topic, the reader is referred to the excellent books by Jenkins and Watts (1968), Hannan (1970), Priestley (1981), and others.
Book Synopsis Time Series Analysis Univariate and Multivariate Methods by : William W. S. Wei
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.
Book Synopsis Time Series Analysis and Its Applications by : Robert H. Shumway
Download or read book Time Series Analysis and Its Applications written by Robert H. Shumway and published by . This book was released on 2014-01-15 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Time Series Analysis by : Terence C. Mills
Download or read book Applied Time Series Analysis written by Terence C. Mills and published by Academic Press. This book was released on 2019-02-08 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public health. Terence Mills provides a practical, step-by-step approach that emphasizes core theories and results without becoming bogged down by excessive technical details. Including univariate and multivariate techniques, Applied Time Series Analysis provides data sets and program files that support a broad range of multidisciplinary applications, distinguishing this book from others. Focuses on practical application of time series analysis, using step-by-step techniques and without excessive technical detail Supported by copious disciplinary examples, helping readers quickly adapt time series analysis to their area of study Covers both univariate and multivariate techniques in one volume Provides expert tips on, and helps mitigate common pitfalls of, powerful statistical software including EVIEWS and R Written in jargon-free and clear English from a master educator with 30 years+ experience explaining time series to novices Accompanied by a microsite with disciplinary data sets and files explaining how to build the calculations used in examples
Book Synopsis Handbook of Time Series Analysis by : Björn Schelter
Download or read book Handbook of Time Series Analysis written by Björn Schelter and published by John Wiley & Sons. This book was released on 2006-12-13 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest developments will profit from this handbook.
Book Synopsis Theory and Applications of Time Series Analysis by : Olga Valenzuela
Download or read book Theory and Applications of Time Series Analysis written by Olga Valenzuela and published by Springer Nature. This book was released on 2020-11-20 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a selection of peer-reviewed contributions on the latest advances in time series analysis, presented at the International Conference on Time Series and Forecasting (ITISE 2019), held in Granada, Spain, on September 25-27, 2019. The first two parts of the book present theoretical contributions on statistical and advanced mathematical methods, and on econometric models, financial forecasting and risk analysis. The remaining four parts include practical contributions on time series analysis in energy; complex/big data time series and forecasting; time series analysis with computational intelligence; and time series analysis and prediction for other real-world problems. Given this mix of topics, readers will acquire a more comprehensive perspective on the field of time series analysis and forecasting. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.
Book Synopsis Essentials of Time Series for Financial Applications by : Massimo Guidolin
Download or read book Essentials of Time Series for Financial Applications written by Massimo Guidolin and published by Academic Press. This book was released on 2018-05-29 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. Provides practical, hands-on examples in time-series econometrics Presents a more application-oriented, less technical book on financial econometrics Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction Features examples worked out in EViews (9 or higher)
Book Synopsis Time Series Analysis: Methods and Applications by : Tata Subba Rao
Download or read book Time Series Analysis: Methods and Applications written by Tata Subba Rao and published by Elsevier. This book was released on 2012-06-26 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Handbook of Statistics' is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with volume 30 dealing with time series.
Book Synopsis Introduction to Time Series and Forecasting by : Peter J. Brockwell
Download or read book Introduction to Time Series and Forecasting written by Peter J. Brockwell and published by Springer Science & Business Media. This book was released on 2006-04-10 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an introduction to time series that emphasizes methods and analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills. Statisticians and students will learn the latest methods in time series and forecasting, along with modern computational models and algorithms.
Book Synopsis Analysis of Financial Time Series by : Ruey S. Tsay
Download or read book Analysis of Financial Time Series written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2010-10-26 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.
Book Synopsis An Introduction to Applied Multivariate Analysis with R by : Brian Everitt
Download or read book An Introduction to Applied Multivariate Analysis with R written by Brian Everitt and published by Springer Science & Business Media. This book was released on 2011-04-23 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Author :Gebhard Kirchgässner Publisher :Springer Science & Business Media ISBN 13 :9783540687351 Total Pages :288 pages Book Rating :4.6/5 (873 download)
Book Synopsis Introduction to Modern Time Series Analysis by : Gebhard Kirchgässner
Download or read book Introduction to Modern Time Series Analysis written by Gebhard Kirchgässner and published by Springer Science & Business Media. This book was released on 2008-08-27 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary.
Book Synopsis Time Series Analysis and Its Applications by : Robert H. Shumway
Download or read book Time Series Analysis and Its Applications written by Robert H. Shumway and published by Springer. This book was released on 2017-04-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.
Book Synopsis Multivariate Reduced-Rank Regression by : Raja Velu
Download or read book Multivariate Reduced-Rank Regression written by Raja Velu and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the area of multivariate analysis, there are two broad themes that have emerged over time. The analysis typically involves exploring the variations in a set of interrelated variables or investigating the simultaneous relation ships between two or more sets of variables. In either case, the themes involve explicit modeling of the relationships or dimension-reduction of the sets of variables. The multivariate regression methodology and its variants are the preferred tools for the parametric modeling and descriptive tools such as principal components or canonical correlations are the tools used for addressing the dimension-reduction issues. Both act as complementary to each other and data analysts typically want to make use of these tools for a thorough analysis of multivariate data. A technique that combines the two broad themes in a natural fashion is the method of reduced-rank regres sion. This method starts with the classical multivariate regression model framework but recognizes the possibility for the reduction in the number of parameters through a restrietion on the rank of the regression coefficient matrix. This feature is attractive because regression methods, whether they are in the context of a single response variable or in the context of several response variables, are popular statistical tools. The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.
Book Synopsis Singular Spectrum Analysis for Time Series by : Nina Golyandina
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 120 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.
