Time Series

A comprehensive overview of modern time series modeling with an emphasis on Bayesian and likelihood methods, this book is unique in that it covers both univariate and multivariate time series methods, including hot topics such as dynamic linear models, hierarchical models and graphical models. The book is mathematically sophisticated but has a good balance of theory and applications, with lots of examples using real data, taken primarily from engineering, finance, and neuroscience. The examples are all implemented using R, with code and datasets available from the web. There are problems at the end of each chapter to help develop understanding of the methods.