ABSTRACT
This chapter discusses key goals of time series analysis with motivating examples from different applied areas. Notation and key concepts related to time series processes are introduced, including the characterization of stationary processes. This is followed by a brief review on likelihood and Bayesian modeling and inference tools which includes a primer on simulation-based methods for posterior inference within the Bayesian framework. The modeling and inference tools are illustrated for the class of first order autoregressive processes.
