ARMA modeling
Purpose
Description
Macro Synopsis
Modules
Related Functions
References
Purpose
Estimates the coefficients of an AR or an ARMA model
Description
An Auto-Regressive (AR) process 
is defined
by
and an Auto-Regressive Moving Average (ARMA) process by
Here, r is a random process (or a noise signal).
"ARMA modeling" estimates the coefficients of an AR or an ARMA
process of given order for an input signal y using the Yule-Walker
and the Durbin approach, respectively.
Input parameters are
- order P of the AR process and
- order Q of the MA process (choose Q=0 for a pure AR model).
The coefficients of the estimated model will appear in the
message window. The signal returned is the residual signal r, i.e.
the estimation error signal.
Macro Synopsis
r = ARMAmodel(y,P,Q);
signal r,y;
int P,Q;
Modules
Statistics
Related Functions
AR simulation, ARIMA modeling,
ARIMAX modeling, ARMA model order,
ARMA simulation,
ARMA spectral density, ARMAX modeling,
ARMAX simulation, NAR modeling,
NARMA modeling.
References
Ljung [10], Conover [11], Graybill
[12], Hollander/Wolfe [13]