1.1
1.2
1.3
1.
1.4
1.5
1.6
Introduction
Synopsis
We introduce basic ideas of time series analysis and
stochastic processes.
Of particular importance are the concepts of stationarity and
the autocovariance and sample autocovariance functions.
Some st
3.1
3.2
3.
3.3
ARMA Models
Synopsis
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
5.1
5.1.1
5.
5.1.3
5.2
5.3
5.4
5.5
Modeling and Forecasting with ARMA
Processes
Synopsis
The determination of an appropriate ARMA(p, q) model to
represent an observed stationary time series involves a
number of interrelated problems.
These include
2.1
2.2
2.3
2.
2.4
2.5
2.6
Stationary Processes
Synopsis
In time series analysis our goal is to predict a series that is
typically not deterministic but contains a random
component.
If this random component is stationary, in the sense of
Denition 1.