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＜電子ブック＞
Introduction to Time Series and Forecasting / by Peter J. Brockwell, Richard A. Davis
(Springer Texts in Statistics. ISSN:21974136)

版	2nd ed. 2002.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2002
本文言語	英語
大きさ	XIV, 437 p : online resource
著者標目	*Brockwell, Peter J author Davis, Richard A author SpringerLink (Online service)
件　名	LCSH:Computer software LCSH:Probabilities LCSH:Statistics  LCSH:Econometrics FREE:Mathematical Software FREE:Probability Theory FREE:Statistical Theory and Methods FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Econometrics FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	Stationary Processes -- ARMA Models -- Spectral Analysis -- Modeling and Forecasting with ARMA Processes -- Nonstationary and Seasonal Time Series Models -- Multivariate Time Series -- State-Space Models -- Forecasting Techniques -- Further Topics -- Erratum 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 HTTP:URL=https://doi.org/10.1007/b97391

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