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Full Description
The goals of this new, second edition of this book are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. An expanded feature of this edition is the inclusion of many nontrivial data sets illustrating the wealth of potential applications to problems in the biological, physical, and social sciences as well as in economics and medicine.
This edition emphasizes a variety of methodological techniques to illustrate solutions to data analysis problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems.
Key Features:
· Presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.
· Detailed R code is included with each numerical example.
· Includes nontrivial data sets.
The book can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability and statistics skills, and math skills at the high school level. All the numerical examples use the R statistical package without assuming the reader has previously used the software.
Contents
Preface 1 Time Series Elements 2 Correlation and Stationary Time Series 3 Time Series Regression and EDA 4 ARMA Models 5 ARIMA Models 6 Spectral Analysis and Filtering 7 Spectral Estimation 8 Additional Topics* Appendix A Probability and Statistics Primer Appendix B Complex Number Primer Hints for Selected Exercises References Index
