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Introduction to Time Series...
Some of the key mathematical results are stated without proof in order to make the underlying theory accessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and nonstationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to nonlinear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis
Monografía
monografia Rebiun25517493 https://catalogo.rebiun.org/rebiun/record/Rebiun25517493 m o d cr nn|008mamaa 100301s2002 nyua obs 001 0 eng GBB7A5695 bnb 814269625 853261718 1052113496 1086479544 1119889102 9780387216577 electronic bk.) 038721657X electronic bk.) 1280187824 9781280187827 9788181284044 8181284046 9780387953519 0387953515 10.1007/b97391 doi NZ1 14977741 AU@ 000051621698 AU@ 000051743078 AU@ 000058032134 NZ1 15626155 com.Palgrave Macmillan.onix.9780387216577 Ingram Content Group Nz eng pn UV0 OCLCO OCLCQ GW5XE OCLCF IDEBK OCLCQ OCLCO OCLCQ NLE UAB U3W OCLCQ LEAUB NLW PBT bicssc MAT029000 bisacsh UFM thema 519.5 23 Brockwell, Peter J. Introduction to Time Series and Forecasting edited by Peter J. Brockwell, Richard A. Davis New York, NY Springer New York 2002 New York, NY New York, NY Springer New York 1 online resource (XVII, 469 pages 150 illustrations) 1 online resource (XVII, 469 pages 150 illustrations) Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Texts in Statistics 1431-875X Includes bibliographical references (pages 423-428) and index Introduction -- Stationary Processes -- ARMA Models -- Spectral Analysis -- Modelling and Forecasting with ARMA Processes -- Nonstationary and Seasonal Time Series Models -- Multivariate Time Series -- State-Space Models -- Forecasting Techniques -- Further Topics -- Appendix A: Random Variables and Probability Distributions -- Appendix B: Statistical Complements -- Appendix C: Mean Square Convergence -- Appendix D: An ITSM Tutorial -- References -- Index Legal Deposit Only available on premises controlled by the deposit library and to one user at any one time The Legal Deposit Libraries (Non-Print Works) Regulations (UK). WlAbNL Some of the key mathematical results are stated without proof in order to make the underlying theory accessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and nonstationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to nonlinear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force. WlAbNL Statistics Mathematical statistics Economics- Statistics Econometrics Série chronologique MATHEMATICS- Probability & Statistics- Time Series Econometrics Economics Mathematical statistics Statistics Tijdreeksen Prognoses Análise de séries temporais Electronic books Statistics Davis, Richard A. Print version 9780387953519 Springer texts in statistics 1431-875X