We introduce basic ideas of time series analysis and
Of particular importance are the concepts of stationarity and
the autocovariance and sample autocovariance functions.
We introduce an important parametric family of stationary
time series, the autoregressive moving-average processes.
For a large class of autocovariance functions (), it is
possible to nd an ARMA process cfw_Xt with
Modeling and Forecasting with ARMA
The determination of an appropriate ARMA(p, q) model to
represent an observed stationary time series involves a
number of interrelated problems.
In time series analysis our goal is to predict a series that is
typically not deterministic but contains a random
If this random component is stationary, in the sense of