Introductory Time Series with R

Introductory Time Series with R

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Rhasacommandlineinterfacethato?ersconsiderableadvantagesovermenu systemsintermsofe?ciencyandspeedoncethecommandsareknownandthe languageunderstood. However,thecommandlinesystemcanbedauntingfor the?rst-timeuser,sothereisaneedforconcisetextstoenablethestudentor analysttomakeprogresswithRintheirareaofstudy. Thisbookaimstoful?l thatneedintheareaoftimeseries toenablethenon-specialisttoprogress, atafairlyquickpace,toalevelwheretheycancon?dentlyapplyarangeof timeseriesmethodstoavarietyofdatasets. Thebookassumesthereader hasaknowledgetypicalofa?rst-yearuniversitystatisticscourseandisbased aroundlecturenotesfromarangeoftimeseriescoursesthatwehavetaught overthelasttwentyyears. Someofthismaterialhasbeendeliveredtopo- graduate?nancestudentsduringaconcentratedsix-weekcourseandwaswell received,soaselectionofthematerialcouldbemasteredinaconcentrated course,althoughingeneralitwouldbemoresuitedtobeingspreadovera completesemester. Thebookisbasedaroundpracticalapplicationsandgenerallyfollowsa similar format for each time series model being studied. First, there is an introductory motivational section that describes practical reasons why the modelmaybeneeded. Second,themodelisdescribedandde?nedinma- ematicalnotation. Themodelisthenusedtosimulatesyntheticdatausing Rcodethatcloselyre?ectsthemodelde?nitionandthen?ttedtothes- theticdatatorecovertheunderlyingmodelparameters. Finally,themodel is?ttedtoanexamplehistoricaldatasetandappropriatediagnosticplots given. By using R, the whole procedure can be reproduced by the reader, 1 anditisrecommendedthatstudentsworkthroughmostoftheexamples. Mathematical derivations are provided in separate frames and starred sec- 1 WeusedtheRpackageSweavetoensurethat,ingeneral,yourcodewillproduce thesameoutputasours. However,forstylisticreasonswesometimeseditedour code;e. g. ,fortheplotstherewillsometimesbeminordi?erencesbetweenthose generatedbythecodeinthetextandthoseshownintheactual?gures. vii viii Preface tionsandcanbeomittedbythosewantingtoprogressquicklytopractical applications. Attheendofeachchapter,aconcisesummaryoftheRc- mands that were used is given followed by exercises. All data sets used in thebook,andsolutionstotheoddnumberedexercises,areavailableonthe websitehttp://www. massey. ac. nz/?pscowper/ts. WethankJohnKimmelofSpringerandtheanonymousrefereesfortheir helpfulguidanceandsuggestions,BrianWebbyforcarefulreadingofthetext andvaluablecomments,andJohnXieforusefulcommentsonanearlierdraft. TheInstituteofInformationandMathematicalSciencesatMasseyUniv- sity and the School of Mathematical Sciences, University of Adelaide, are acknowledgedforsupportandfundingthatmadeourcollaborationpossible. Paul thanks his wife, Sarah, for her continual encouragement and support duringthewritingofthisbook,andourson,Daniel,anddaughters,Lydia andLouise,forthejoytheybringtoourlives. AndrewthanksNataliefor providinginspirationandherenthusiasmfortheproject. PaulCowpertwaitandAndrewMetcalfe MasseyUniversity,Auckland,NewZealand UniversityofAdelaide,Australia December2008 Contents Preface...vii 1 TimeSeriesData...1 1. 1 Purpose...1 1. 2 Timeseries...2 1. 3 Rlanguage...3 1. 4 Plots,trends,andseasonalvariation ...4 1. 4. 1 A?yingstart:Airpassengerbookings...4 1. 4. 2 Unemployment:Maine...7 1. 4. 3 Multipletimeseries:Electricity,beerandchocolatedata 10 1. 4. 4 Quarterlyexchangerate:GBPtoNZdollar...14 1. 4. 5 Globaltemperatureseries ...16 1. 5 Decompositionofseries ...19 1. 5. 1 Notation...19 1. 5. 2 Models...1 9 1. 5. 3 Estimatingtrendsandseasonale?ects ...20 1. 5. 4 Smoothing ...21 1. 5. 5 DecompositioninR...22 1. 6 Summaryofcommandsusedinexamples...24 1. 7 Exercises...24 2 Correlation...27 2. 1 Purpose...27 2. 2 Expectationandtheensemble...

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Product details

  • Paperback | 272 pages
  • 154.94 x 231.14 x 17.78mm | 294.83g
  • Springer-Verlag New York Inc.
  • New York, NY, United States
  • English
  • biography
  • 0387886974
  • 9780387886978
  • 260,324

Review quote

From the reviews: "The 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. 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. After that, they fit the class of models to either environmental, finance, economics, or physics data. There are many applications to climate change and oceanography. The R programs for the simulations are given even if there are R functions that would do the simulation. All examples given can be reproduced by the reader using the code all chapters. Exercises at the end of each chapter are interesting, involving simulation, estimation, description, graphical analysis, and some theory. Data sets used throughout the book are available in a web site or come with base R or the R packages used. 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. ..." (Journal of Statistical Software, January 2010, Vol. 32, Book Review 4) "Later year undergraduates, beginning graduate students, and researchers and graduate students in any discipline needing to explore and analyse time series data. This very readable text covers a wide range of time series topics, always however within a theoretical framework that makes normality assumptions. The range of models that are discussed is unusually wide for an introductory text. ... The mathematical theory is remarkably complete ... . This text is recommended for its wide-ranging and insightful coverage of time series theory and practice." (John H. Maindonald, International Statistical Review, Vol. 78 (3), 2010) "The authors present a textbook for students and applied researchers for time series analysis and linear regression analysis using R as the programming and command language. ... The book is written for students with knowledge of a first-year university statistics course in New-Zealand and Australia, but it also might serve as a useful tools for applied researchers interested in empirical procedures and applications which are not menu driven as it is the case for most econometric software packages nowadays." (Herbert S. Buscher, Zentralblatt MATH, Vol. 1179, 2010)

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Back cover copy

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. 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: // 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. 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. 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. Both authors have extensive experience of teaching time series to students at all levels.

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Table of contents

Time Series Data.- Correlation.- Forecasting Strategies.- Basic Stochastic Models.- Regression.- Stationary Models.- Non-stationary Models.- Long-Memory Processes.- Spectral Analysis.- System Identification.- Multivariate Models.- State Space Models.

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