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020 _a9780387886985
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024 7 _a10.1007/978-0-387-88698-5
_2doi
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
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072 7 _aPBWL
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082 0 4 _a519.2
_223
100 1 _aCowpertwait, Paul S.P.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aIntroductory Time Series with R
_h[electronic resource] /
_cby Paul S.P. Cowpertwait, Andrew V. Metcalfe.
250 _a1st ed. 2009.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2009.
300 _aXVI, 256 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUse R!,
_x2197-5736
505 0 _aTime 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.
520 _aYearly 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://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. 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.
650 0 _aProbabilities.
650 0 _aStatistics .
650 0 _aMathematical statistics.
650 0 _aMarketing.
650 0 _aEconometrics.
650 0 _aSignal processing.
650 0 _aImage processing.
650 0 _aSpeech processing systems.
650 1 4 _aProbability Theory and Stochastic Processes.
_0http://scigraph.springernature.com/things/product-market-codes/M27004
650 2 4 _aStatistical Theory and Methods.
_0http://scigraph.springernature.com/things/product-market-codes/S11001
650 2 4 _aProbability and Statistics in Computer Science.
_0http://scigraph.springernature.com/things/product-market-codes/I17036
650 2 4 _aMarketing.
_0http://scigraph.springernature.com/things/product-market-codes/513000
650 2 4 _aEconometrics.
_0http://scigraph.springernature.com/things/product-market-codes/W29010
650 2 4 _aSignal, Image and Speech Processing.
_0http://scigraph.springernature.com/things/product-market-codes/T24051
700 1 _aMetcalfe, Andrew V.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387889474
776 0 8 _iPrinted edition:
_z9780387886978
830 0 _aUse R!,
_x2197-5736
856 4 0 _uhttps://doi.org/10.1007/978-0-387-88698-5
912 _aZDB-2-SMA
999 _c17805
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942 _cebook