Book description
Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals.
Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, Basic Data Analysis for Time Series with R also features:
Real-world examples to provide readers with practical hands-on experience
Multiple R software subroutines employed with graphical displays
Numerous exercise sets intended to support readers understanding of the core concepts
Specific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets
Table of contents
- PREFACE
- ACKNOWLEDGMENTS
- PART I BASIC CORRELATION STRUCTURES
-
PART II ANALYSIS OF PERIODIC DATA AND MODEL SELECTION
- 7 Review of Transcendental Functions and Complex Numbers
- 8 The 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
- 13.1 Introduction
- 13.2 The Two-Sample t-Test—UNCUT and Patch-Cut Forest
- 13.3 The Second Sleuth Case—Global Warming, A Simple Regression
- 13.4 The Semmelweis Intervention
- 13.5 The NYC Temperatures (Adjusted)
- 13.6 The Boise River Flow Data: Model Selection With Filtering
- 13.7 Implications of AR(1) Adjustments and the “Skip” Method
- 13.8 Summary
- Exercises
-
PART III COMPLEX TEMPORAL STRUCTURES
- 14 The backshift operator, the impulse response function, and general ARMA models
- 15 The Yule–Walker Equations and the Partial Autocorrelation Function
-
16 Modeling philosophy and Complete Examples
- 16.1 Modeling overview
- 16.2 A complex periodic model—Monthly river flows, Furnas 1931–1978
- 16.3 A modeling example—trend and periodicity: CO2 levels at Mauna Lau
- 16.4 Modeling periodicity with a possible intervention—two examples
- 16.5 Periodic models: monthly, weekly, and daily averages
- 16.6 Summary
- Exercises
- PART IV SOME DETAILED AND COMPLETE EXAMPLES
- REFERENCES
- INDEX
- End User License Agreement
Product information
- Title: Basic Data Analysis for Time Series with R
- Author(s):
- Release date: July 2014
- Publisher(s): Wiley
- ISBN: 9781118422540
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