Time Series Analysis and Its Applications

Time Series Analysis and Its Applications: With R Examples offers a balanced and comprehensive treatment of both time and frequency domain methods, underpinned by a solid theoretical framework. The text illustrates its concepts with numerous examples drawn from nontrivial data, tackling challenges such as identifying natural versus anthropogenic climate change, analyzing fMRI data in pain perception experiments, and monitoring aspects of nuclear test ban treaties. Designed primarily for graduate-level students in the physical, biological, and social sciences as well as in statistics, the book also contains elements that can serve as an introduction for undergraduates. Its clear division between theory and methodology allows the reader to engage with the material at different depths, depending on prior experience and study requirements. In addition to classical topics like time series regression, ARIMA models, spectral analysis, and state-space models, the text delves into modern developments such as categorical time series analysis, multivariate spectral methods, long memory processes, nonlinear models, resampling techniques, GARCH models, stochastic volatility, wavelets, and Monte Carlo Markov chain integration methods. This edition further benefits from expanded content on state-space modeling, ARMAX models, and regression with autocorrelated errors, along with a new section on testing for unit roots. Enhanced integration of the freeware statistical package R means that R code accompanies nearly all numerical examples, and a downloadable file provides all necessary datasets and scripts. An appendix is also included, offering both a reference for these R tools and a tutorial on R commands and time series analysis techniques.