Time series analysis : with applications in R / Jonathan D. Cryer, Kung-Sik Chan.Material type: TextSeries: Springer texts in statisticsPublication details: New York : Springer, c2008. Edition: 2nd ed.Description: xiii, 491 p. : il., map ; 25 cmISBN: 0387759581 ; 9780387759586 Subject(s): Time-series analysis -- Data processing | Análisis de series temporales | R (Computer program language) | R (Lenguaje de programación)
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The book was developed for a one-semester course usually attended by students in statistics, economics, business, engineering, and quantitative social sciences. Basic applied statistics is assumed through multiple regression. Calculus is assumed only to the extent of minimizing sums of squares but a calculus-based introduction to statistics is necessary for a thorough understanding of some of the theory. However, required facts concerning expectation, variance, covariance, and correlation are reviewed in appendices. Also, conditional expectation properties and minimum mean square error prediction are developed in appendices. Actual time series data drawn from various disciplines are used throughout the book to illustrate the methodology. The book contains additional topics of a more advanced nature that could be selected for inclusion in a course if the instructor so chooses. Fully integrates time series theory with applications. Has an associated R package, TSA, to carry out the required computations and graphics. Uses numerous interesting real datsets to illustrate all of the ideas
Bibliografía: (p. 477-486) . - índices
Introduction -- Fundamental concepts -- Trends -- Models for stationary time series -- Models for nonstationary time series -- Model specification -- Parameter estimation -- Model diagnostics -- Forecasting -- Seasonal models -- Time series regression models -- Time series models of heteroscedasticity -- Introduction to spectral analysis -- Estimating the spectrum -- Threshold models -- Appendix: an introduction to R.
Índice: Introduction. Fundamental Concepts. Trends. Models for Stationary Time Series. Models for Nonstationary Time Series. Model Specification. Parameter Estimation. Model Diagnostics. Forecasting. Seasonal Models. Time Series Regression Models. Time Series Models of Heteroscedasticity. Introduction to Spectral Analysis. Estimating the Spectrum. Threshold Models.
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