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 regressionbased 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 timedomain 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:
Realworld examples to provide readers with practical handson 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 BiasVariance Tradeoff, and the Smoothed Periodogram
 10 A Regression Model for Periodic Data
 11 Model Selection and CrossValidation
 12 Fitting Fourier series

13 Adjusting for AR(1) Correlation in Complex Models
 13.1 Introduction
 13.2 The TwoSample tTest—UNCUT and PatchCut ForestTest—UNCUT and PatchCut 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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