This edition contains a large number of additions and corrections scattered throughout the text, including the incorporation of a new chapter on state-space mod

This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA m

Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowled

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulati

Here is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. It detail