Time Series Analysis Univariate And Multivariate Methods

Author : William W. S. Wei
Publisher : Addison-Wesley Longman
Page : 648 pages
File Size : 54,8 Mb
Release : 2006
Category : Time-series analysis
ISBN : UCSD:31822034236901

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Time Series Analysis by William W. S. Wei Pdf

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.

Author : William W. S. Wei
Publisher : John Wiley & Sons
Page : 536 pages
File Size : 44,6 Mb
Release : 2019-03-18
Category : Mathematics
ISBN : 9781119502852

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Multivariate Time Series Analysis and Applications by William W. S. Wei Pdf

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.

Author : Chris Chatfield,Haipeng Xing
Publisher : CRC Press
Page : 398 pages
File Size : 53,9 Mb
Release : 2019-04-25
Category : Mathematics
ISBN : 9781498795647

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The Analysis of Time Series by Chris Chatfield,Haipeng Xing Pdf

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. It also presents many examples and implementations of time series models and methods to reflect advances in the field. Highlights of the seventh edition: A new chapter on univariate volatility models A revised chapter on linear time series models A new section on multivariate volatility models A new section on regime switching models Many new worked examples, with R code integrated into the text The book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance.

Author : David R. Brillinger
Publisher : SIAM
Page : 556 pages
File Size : 50,8 Mb
Release : 2001-09-01
Category : Mathematics
ISBN : 9780898715019

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Time Series by David R. Brillinger Pdf

This text employs basic techniques of univariate and multivariate statistics for the analysis of time series and signals.

Author : Daniel Peña,George C. Tiao,Ruey S. Tsay
Publisher : John Wiley & Sons
Page : 494 pages
File Size : 51,9 Mb
Release : 2011-01-25
Category : Mathematics
ISBN : 9781118031223

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A Course in Time Series Analysis by Daniel Peña,George C. Tiao,Ruey S. Tsay Pdf

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

Author : Ruey S. Tsay
Publisher : John Wiley & Sons
Page : 414 pages
File Size : 49,8 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9781118617755

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Multivariate Time Series Analysis by Ruey S. Tsay Pdf

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.

Author : Ignacio Rojas,Héctor Pomares,Olga Valenzuela
Publisher : Springer
Page : 340 pages
File Size : 53,5 Mb
Release : 2018-10-03
Category : Business & Economics
ISBN : 9783319969442

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Time Series Analysis and Forecasting by Ignacio Rojas,Héctor Pomares,Olga Valenzuela Pdf

This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.

Author : Björn Schelter,Matthias Winterhalder,Jens Timmer
Publisher : John Wiley & Sons
Page : 514 pages
File Size : 44,9 Mb
Release : 2006-12-13
Category : Science
ISBN : 9783527609512

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Handbook of Time Series Analysis by Björn Schelter,Matthias Winterhalder,Jens Timmer Pdf

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.

Author : Robert H. Shumway,David S. Stoffer
Publisher : Unknown
Page : 568 pages
File Size : 52,5 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 1475732627

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Time Series Analysis and Its Applications by Robert H. Shumway,David S. Stoffer Pdf

Author : Massimo Guidolin,Manuela Pedio
Publisher : Academic Press
Page : 435 pages
File Size : 53,9 Mb
Release : 2018-05-29
Category : Business & Economics
ISBN : 9780128134108

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Essentials of Time Series for Financial Applications by Massimo Guidolin,Manuela Pedio Pdf

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)

Author : Peter J. Brockwell,Richard A. Davis
Publisher : Springer Science & Business Media
Page : 429 pages
File Size : 49,6 Mb
Release : 2013-03-14
Category : Mathematics
ISBN : 9781475725261

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Introduction to Time Series and Forecasting by Peter J. Brockwell,Richard A. Davis Pdf

Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

Author : Gebhard Kirchgässner,Jürgen Wolters
Publisher : Springer Science & Business Media
Page : 288 pages
File Size : 50,7 Mb
Release : 2008-08-27
Category : Business & Economics
ISBN : 3540687351

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Introduction to Modern Time Series Analysis by Gebhard Kirchgässner,Jürgen Wolters Pdf

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.

Author : Janet M. Box-Steffensmeier,John R. Freeman,Matthew P. Hitt,Jon C. W. Pevehouse
Publisher : Cambridge University Press
Page : 297 pages
File Size : 42,7 Mb
Release : 2014-12-22
Category : Political Science
ISBN : 9780521871167

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Time Series Analysis for the Social Sciences by Janet M. Box-Steffensmeier,John R. Freeman,Matthew P. Hitt,Jon C. W. Pevehouse Pdf

This book provides instruction and examples of the core methods in time series econometrics, drawing from several main fields of the social sciences.

Author : Robert H. Shumway,David S. Stoffer
Publisher : Springer Science & Business Media
Page : 560 pages
File Size : 48,9 Mb
Release : 2013-03-14
Category : Mathematics
ISBN : 9781475732610

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Time Series Analysis and Its Applications by Robert H. Shumway,David S. Stoffer Pdf

A balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems, such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. Although designed as a text for graduate level students in statistics and the physical, biological and social sciences, some parts of the book will also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels, and the material has been updated by adding modern developments involving categorical time series analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. The book is supplemented by data and an exploratory time series analysis program ASTSA for Windows that can be downloaded from the Web as freeware.

Author : Gebhard Kirchgässner,Jürgen Wolters,Uwe Hassler
Publisher : Springer Science & Business Media
Page : 326 pages
File Size : 42,7 Mb
Release : 2012-10-09
Category : Business & Economics
ISBN : 9783642334351

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Introduction to Modern Time Series Analysis by Gebhard Kirchgässner,Jürgen Wolters,Uwe Hassler Pdf

This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.