Features several useful and newly developed techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models.

This graduate textbook introduces linear processes in terms of the Wold expansion and an infinite moving-average representation, examines the relationships between Wold expansions and state space representations, and reviews several methodologies for estimating time series models. Later chapters discuss nonlinear time series for analyzing financial instruments, the prediction of linear and nonlinear processes, nonstationary time series models, seasonal patterns, time series regression, the effects of data gaps on the analysis of long-memory processes, and non-Gaussian time series. Annotation 2016 Ringgold, Inc., Portland, OR (protoview.com)