000			03675nam a22005175i 4500
001			978-0-387-88698-5
003			DE-He213
005			20251006084429.0
007			cr nn 008mamaa
008			100301s2009 xxu| s |||| 0|eng d
020			_a9780387886985
020			_a99780387886985
024	7		_a10.1007/978-0-387-88698-5 _2doi
100	1		_aMetcalfe, Andrew V. _eauthor.
245	1	0	_aIntroductory Time Series with R _h[electronic resource] / _cby Andrew V. Metcalfe, Paul S.P. Cowpertwait.
264		1	_aNew York, NY : _bSpringer New York, _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
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	_aSTATISTICS.
650		0	_aCOMPUTER SCIENCE.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aECONOMETRICS.
650		0	_aMARKETING.
650	1	4	_aSTATISTICS.
650	2	4	_aENVIRONMENTAL MONITORING/ANALYSIS.
650	2	4	_aSIGNAL, IMAGE AND SPEECH PROCESSING.
650	2	4	_aECONOMETRICS.
650	2	4	_aMARKETING.
650	2	4	_aPROBABILITY AND STATISTICS IN COMPUTER SCIENCE.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
700	1		_aCowpertwait, Paul S.P. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387886978
830		0	_aUse R
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-88698-5 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59395 _d59395