## ARMAX simulation

Purpose

Description

Macro Synopsis

Modules

Related Functions

References

### Purpose

Simulate an ARMAX process

### Description

"ARMAX simulation" simulates an autoregressive moving-average with eXternal / eXtra (ARMAX) process according to

using two signals x and z, where x is the input signal (usually white noise) and z is the eXternal / eXtra signal.

The parameters are

- AR order (number of coefficients) n of the process
- AR coefficients a1, a2, ..., aP
- MA order (number of coefficients) m of the process
- MA coefficients b1, b2, ..., bQ
- eXtra/eXternal order (number of coefficients) k of the process
- eXtra/eXternal coefficients c1, c2, ..., cR
- constant term K.

**Warning:** *Note that if the input signals have different x-axis scales (sampling periods), the signal with the largest scale will be adapted automatically by interpolating between successive sample points. The type of interpolation can be set in the Basic Options menu.*

### Macro Synopsis

`y =
ARMAXsim([x,z],n,"a1,a2,...,an",m,"b1,b2,...,bn",k,"c1,c2,...,ck","K");`

signal x,y,z;

int m,n,k;

string a,b,c,K;

### Modules

Statistics

### Related Functions

AR simulation, ARIMA modeling,
ARIMAX modeling, ARMA model order,
ARMA modeling, ARMA simulation,
ARMA spectral density, ARMAX modeling,
NAR modeling, NARMA modeling.

### References

Ljung [10], Conover [11], Graybill
[12], Hollander/Wolfe [13]