ABSTRACT
This chapter deals with some of the oldest and most useful of all stationary process models, the autoregressive AR-model and the moving average MAmodel. They form the basic elements in time series analysis of both stationary and non-stationary sequences, including model identification and parameter estimation. Predictions can be made in an algorithmically simple way in these models, and they can be used for efficient Monte Carlo simulation.
