Basic data analysis for time series with R / DeWayne R. Derryberry.
Material type:
TextPublication details: New Jersey : John Wiley, 2014.Description: xviii, 299 p. : illustrations ; 25 cmISBN: - 9781118422540 (hardback)
- 000SA.3 23 D438
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.3 D438 (Browse shelf(Opens below)) | Available | 136669 |
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| 000SA.3 C334 State-space methods for time series analysis : | 000SA.3 D263 Handbook of discrete-valued time series / | 000SA.3 D319 Elements of nonlinear time series analysis and forecasting / | 000SA.3 D438 Basic data analysis for time series with R / | 000SA.3 D728 Nonlinear times series : | 000SA.3 G633 Multivariate time series with linear state space structure / | 000SA.3 In61 Modeling and stochastic learning for forecasting in high dimensions : |
Includes bibliographical references and index.
Part I Basic correlation structures --
1 R Basics --
2 Review of regression and more about R --
3 Modeling approach taken in this book and some examples of typical serially correlated data --
4 Some comments on assumptions 4.1 Introduction --
5 Autocorrelation function and AR(1), AR(2) models --
6 Moving average models MA(1) and MA(2) --
Part II Analysis of periodic data and model selection --
7 Review of transcendental functions and complex numbers --
8 Power spectrum and the periodogram --
9 Smoothers, the bias-variance tradeoff, and the smoothed periodogram --
10 A regression model for periodic data --
11 Model selection and cross-validation --
12 Fitting fourier series --
13 Adjusting for AR(1) correlation in complex models --
Part III Complex temporal structures --
14 Backshift operator, the impulse response function, and general ARMA models --
15 Yule-Walker equations and the partial autocorrelation function --
16 Modeling philosophy and complete examples --
Part IV Some detailed and complete examples --
17 Wolf's sunspot number data --
18 An analysis of some prostate and breast cancer data --
19 Christopher Tennant/Ben Crosby watershed data --
20 Vostok ice core data --
Appendix A Using datamarket --
Appendix B AIC is PRESS! --
Appendix C A 15-minute tutorial on nonlinear optimization --
References --
Index.
This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book. The content includes modern topics such as wavelets, Fourier series, and Akaike's Information Criterion (AIC), which is not typical of current-day "classics." Applications to a variety of scientific fields are showcased. Exercise sets are well crafted with the express intent of supporting pedagogy through recognition and repetition. R subroutines are employed as the software and graphics tool of choice. Brevity is a key component to the retention of the subject matter. The book presumes knowledge of linear algebra, probability, data analysis, and basic computer programming.
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