Time Series Analysis and Its Applications

With R Examples

Author: Robert H. Shumway,David S. Stoffer

Publisher: Springer

ISBN: 144197864X

Category: Mathematics

Page: 596

View: 8513

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Time Series Analysis and Its Applications 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 to be useful as a text 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, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. The third edition includes a new section on testing for unit roots and the material on state-space modeling, ARMAX models, and regression with autocorrelated errors have been expanded. Also new to this edition is the enhanced use of the freeware statistical package R. In particular, R code is now included in the text for nearly all of the numerical examples. Data sets and additional R scripts are now provided in one file that may be downloaded via the World Wide Web. This R supplement is a small compressed file that can be loaded easily into R making all the data sets and scripts available to the user with one simple command. The website for the text includes the code used in each example so that the reader may simply copy-and-paste code directly into R. Appendix R, which is new to this edition, provides a reference for the data sets and our R scripts that are used throughout the text. In addition, Appendix R includes a tutorial on basic R commands as well as an R time series tutorial.
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Time Series Analysis

With Applications in R

Author: Jonathan D. Cryer,Kung-Sik Chan

Publisher: Springer Science & Business Media

ISBN: 0387759581

Category: Business & Economics

Page: 491

View: 9812

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This book presents an accessible approach to understanding time series models and their applications. The ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment.
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New Frontiers in High Performance Computing and Big Data

Author: G. Fox,V. Getov,L. Grandinetti

Publisher: IOS Press

ISBN: 1614998167

Category: Computers

Page: 272

View: 1687

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For the last four decades, parallel computing platforms have increasingly formed the basis for the development of high performance systems primarily aimed at the solution of intensive computing problems, and the application of parallel computing systems has also become a major factor in furthering scientific research. But such systems also offer the possibility of solving the problems encountered in the processing of large-scale scientific data sets, as well as in the analysis of Big Data in the fields of medicine, social media, marketing, economics etc. This book presents papers from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2016. The workshop covered a wide range of topics and new developments related to the solution of intensive and large-scale computing problems, and the contributions included in this volume cover aspects of the evolution of parallel platforms and highlight some of the problems encountered with the development of ever more powerful computing systems. The importance of future large-scale data science applications is also discussed. The book will be of particular interest to all those involved in the development or application of parallel computing systems.
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Stochastic Models for Time Series

Author: Paul Doukhan

Publisher: Springer

ISBN: 3319769383

Category: Mathematics

Page: 308

View: 5473

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This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures. Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series.
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Introductory Time Series with R

Author: Paul S.P. Cowpertwait,Andrew V. Metcalfe

Publisher: Springer Science & Business Media

ISBN: 0387886982

Category: Mathematics

Page: 256

View: 4582

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This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.
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Applied Multivariate Statistics with R

Author: Daniel Zelterman

Publisher: Springer

ISBN: 3319140930

Category: Medical

Page: 393

View: 9766

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This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.
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