
Author 
Paul S.P. Cowpertwait 
ISBN10 
9780387886985 
Year 
20090528 
Pages 
256 
Language 
en 
Publisher 
Springer Science & Business Media 
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This book gives you a stepbystep 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/PaulCowpertwait/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.

Author 
Paul S.P. Cowpertwait 
ISBN10 
0387886974 
Year 
20090609 
Pages 
256 
Language 
en 
Publisher 
Springer 
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This book gives the reader a stepbystep introduction to analyzing time series using the open source software R. Each time series model is illustrated through practical applications addressing contemporary issues, and is defined in mathematical notation.

Author 
Robert H. Shumway 
ISBN10 
9781441978653 
Year 
20101111 
Pages 
596 
Language 
en 
Publisher 
Springer Science & Business Media 
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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 statespace 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 Markov chain Monte Carlo integration methods. The third edition includes a new section on testing for unit roots and the material on statespace modeling, ARMAX models, and regression with autocorrelated errors has 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 copyandpaste 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.

Author 
Jonathan D. Cryer 
ISBN10 
9780387759586 
Year 
20080404 
Pages 
491 
Language 
en 
Publisher 
Springer Science & Business Media 
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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.

Author 
Christian Kleiber 
ISBN10 
0387773185 
Year 
20081210 
Pages 
222 
Language 
en 
Publisher 
Springer Science & Business Media 
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R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an awardwinning language initially  veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is opensource software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the opensource philosophy make R an ideal environment for reproducible econometric research.

Author 
Peter Dalgaard 
ISBN10 
9780387790541 
Year 
20080627 
Pages 
364 
Language 
en 
Publisher 
Springer Science & Business Media 
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This book provides an elementarylevel introduction to R, targeting both nonstatistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one and twosample tests with continuous data, regression analysis, oneand twoway analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

Author 
Giovanni Petris 
ISBN10 
9780387772387 
Year 
20090612 
Pages 
252 
Language 
en 
Publisher 
Springer Science & Business Media 
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State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

Author 
Douglas C. Montgomery 
ISBN10 
9781118745151 
Year 
20150421 
Pages 
672 
Language 
en 
Publisher 
John Wiley & Sons 
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Praise for the First Edition "…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze timeoriented data and construct realworld short to mediumterm statistical forecasts. Authored by highlyexperienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of timeoriented data New material on frequency domain and spatial temporal data analysis Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upperundergraduate and graduatelevels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

Author 
K. Gerald van den Boogaart 
ISBN10 
9783642368097 
Year 
20130629 
Pages 
258 
Language 
en 
Publisher 
Springer Science & Business Media 
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This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package “compositions,” it is also a general introductory text on Compositional Data Analysis. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software. The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics. Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is selfcontained.

Author 
Walter Zucchini 
ISBN10 
9781482253849 
Year 
20160622 
Pages 
370 
Language 
en 
Publisher 
CRC Press 
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Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as generalpurpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuousvalued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semiMarkov models and models with continuousvalued state process New case studies on animal movement, rainfall occurrence and capture–recapture data

Author 
Rob J Hyndman 
ISBN10 
9780987507105 
Year 
20140920 
Pages 
291 
Language 
en 
Publisher 
OTexts 
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Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Author 
Wayne A. Woodward 
ISBN10 
9781439897690 
Year 
20111026 
Pages 
564 
Language 
en 
Publisher 
CRC Press 
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Virtually any random process developing chronologically can be viewed as a time series. In economics, closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroomtested, Applied Time Series Analysis includes examples across a variety of fields, develops theory, and provides software to address time series problems in a broad spectrum of fields. The authors organize the information in such a format that graduate students in applied science, statistics, and economics can satisfactorily navigate their way through the book while maintaining mathematical rigor. One of the unique features of Applied Time Series Analysis is the associated software, GWWINKS, designed to help students easily generate realizations from models and explore the associated model and data characteristics. The text explores many important new methodologies that have developed in time series, such as ARCH and GARCH processes, time varying frequencies (TVF), wavelets, and more. Other programs (some written in R and some requiring Splus) are available on an associated website for performing computations related to the material in the final four chapters.

Author 
Christian Ritz 
ISBN10 
9780387096162 
Year 
20081211 
Pages 
148 
Language 
en 
Publisher 
Springer Science & Business Media 
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 Coherent and unified treatment of nonlinear regression with R.  Examplebased approach.  Wide area of application.

Author 
Peter J. Brockwell 
ISBN10 
3319298526 
Year 
20160920 
Pages 
425 
Language 
en 
Publisher 
Springer 
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This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windowsbased computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series modelbuilding are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menudriven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and statespace models, with an optional chapter on spectral analysis. Many additional special topics are also covered. New to this edition: A chapter devoted to Financial Time Series Introductions to Brownian motion, Lévy processes and Itô calculus An expanded section on continuoustime ARMA processes Peter J. Brockwell and Richard A. Davis are Fellows of the American Statistical Association and the Institute of Mathematical Statistics and elected members of the International Statistics Institute. Richard A. Davis is the current President of the Institute of Mathematical Statistics and, with W.T.M. Dunsmuir, winner of the Koopmans Prize. Professors Brockwell and Davis are coauthors of the widely used advanced text, Time Series: Theory and Methods, Second Edition (SpringerVerlag, 1991). From reviews of the first edition:“/div> This book, like a good science fiction novel, is hard to put down.... Fascinating examples hold one’s attention and are taken from an astonishing variety of topics and fields.... Given that time series forecasting is really a simple idea, it is amazing how much beautiful mathematics this book encompasses. Each chapter is richly filled with examples that serve to illustrate and reinforce the basic concepts. The exercises at the end of each chapter are well designed and make good use of numerical problems. Combined with the ITSM package, this book is ideal as a textbook for the selfstudy student or the introductory course student. Overall then, as a text for a universitylevel course or as a learning aid for an industrial forecaster, I highly recommend the book. —SIAM Review In addition to including ITSM, the book details all of the algorithms used in the package—a quality which sets this text apart from all others at this level. This is an excellent idea for at least two reasons. It gives the practitioner the opportunity to use ITSM more intelligently by providing an extra source of intuition for understanding estimation and forecasting, and it allows the more adventurous practitioners to code their own algorithms for their individual purposes.... Overall I find Introduction to Time Series and Forecasting to be a very useful and enlightening introduction to time series. —Journal of the American Statistical Association divThe emphasis is on handson experience and the friendly software that accompanies the book serves the purpose admirably.... The authors should be congratulated for making the subject accessible and fun to learn. The book is a pleasure to read and highly recommended. I regard it as the best introductory text in town. —Short Book Reviews, International Statistical Review

Author 
Göran Broström 
ISBN10 
9781439831670 
Year 
20120403 
Pages 
236 
Language 
en 
Publisher 
CRC Press 
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With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using reallife examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times. Features Introduces parametric proportional hazards models with baseline distributions like the Weibull, Gompertz, Lognormal, and Piecewise constant hazard distributions, in addition to traditional Cox regression Presents mathematical details as well as technical material in an appendix Includes real examples with applications in demography, econometrics, and epidemiology Provides a dedicated R package, eha, containing special treatments, including making cuts in the Lexis diagram, creating communal covariates, and creating period statistics A muchneeded primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.