000			02005nam a22002297a 4500
005			20250128083545.0
008			250128b2009 |||||||| |||| 00| 0 eng d
020			_a9780387886978
040			_aB-IKIAM
041			_aENG
082			_a519.5 _bC876i
100			_92936 _aPaul S. P. Cowpertwait
245			_aIntroductory Time Series with R _cPaul S. P. Cowpertwait; Andrew V. Metcalfe
250			_a1°Edición
260			_aAustralia _bSpringer _c2009
300			_a272 páginas _bFiguras, tablas _c23 cm
505			_aContents -- Preface -- 1.Time Series Data -- 2.Correlation -- 3.Forecasting Strategies -- 4.Basic Stochastic Models -- 5.Regression -- 6.Stationary Models -- 7.Non-stationary Models -- 8.Long-Memory Processes -- 9.Spectral Analysis -- 10.System Identification -- 11.Multivariate Models -- 12.State Space Models -- References -- Index.
520			_aThis 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.
650		0	_aINTRODUCTORY
700			_92938 _aAndrew V. Metcalfe
942			_2ddc _aB-IKIAM _b06-01-2025 _cBK _zK.R
999			_c2404 _d2404