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This text is recommended for its wide-ranging and insightful coverage of time series theory and practice.” (John H. … The mathematical theory is remarkably complete …. The range of models that are discussed is unusually wide for an introductory text. This very readable text covers a wide range of time series topics, always however within a theoretical framework that makes normality assumptions. “Later year undergraduates, beginning graduate students, and researchers and graduate students in any discipline needing to explore and analyse time series data. …” (Journal of Statistical Software, January 2010, Vol. The book is a great guide to those wishing to get a basic introduction to modern time series modeling in practice, and in a short amount of time. Data sets used throughout the book are available in a web site or come with base R or the R packages used.
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Exercises at the end of each chapter are interesting, involving simulation, estimation, description, graphical analysis, and some theory.
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All examples given can be reproduced by the reader using the code provided…in all chapters. The R programs for the simulations are given even if there are R functions that would do the simulation. There are many applications to climate change and oceanography. After that, they fit the class of models to either environmental, finance, economics, or physics data. The authors explain the models by first giving a basic theoretical introduction followed by simulation of data from a particular model and fitting the latter to the simulated data to recover the parameters.
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“The book…gives a very broad and practical overview of the most common models for time series analysis in the time domain and in the frequency domain, with emphasis on how to implement them with base R and existing R packages such as Rnlme, MASS, tseries, fracdiff, mvtnorm, vars, and sspir. Both authors have extensive experience of teaching time series to students at all levels.
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Andrew Metcalfe is an associate professor in the School of Mathematical Sciences at the University of Adelaide, and an author of six statistics text books and numerous research papers. Paul Cowpertwait is an associate professor in mathematical sciences (analytics) at Auckland University of Technology with a substantial research record in both the theory and applications of time series and stochastic models. 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. All the data sets used in the book are available on the website. By using R, the whole procedure can be reproduced by the reader. Finally, the model is used to analyse observed data taken from a practical application. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. 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. Each time series model is motivated with practical applications, and is defined in mathematical notation.
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This book gives you a step-by-step introduction to analysing time series using the open source software R. Yearly global mean temperature and ocean levels, daily share prices, and the signals transmitted back to Earth by the Voyager space craft are all examples of sequential observations over time known as time series.