com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma

Class ARMAModel

java.lang.Object

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

ARModel, MAModel

public class ARMAModel
extends ARIMAModel

This class represents a univariate ARMA model.

See Also:

Wikipedia: Autoregressive moving average model, Serialized Form

Field Summary

Fields inherited from class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel

AR, MA, mu, psi, sigma

Constructor Summary

Constructors

Constructor and Description
ARMAModel(ARMAModel that) Copy constructor.
ARMAModel(double[] AR, double[] MA) Construct a zero-intercept (mu) univariate ARMA model with unit variance.
ARMAModel(double[] AR, double[] MA, double sigma) Construct a zero-intercept (mu) univariate ARMA model.
ARMAModel(double mu, double[] AR, double[] MA) Construct a univariate ARMA model with unit variance.
ARMAModel(double mu, double[] AR, double[] MA, double sigma) Construct a univariate ARMA model.

Method Summary

Modifier and Type	Method and Description
double	armaMean(double[] arLags, double[] maLags) Compute the univariate ARMA conditional mean.
double	armaMeanNoIntercept(double[] arLags, double[] maLags) Compute the zero-intercept (mu) univariate ARMA conditional mean.

Methods inherited from class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel

getArma

Methods inherited from class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel

AR, AR, d, getArmax, MA, MA, maxPQ, mu, p, psi, q, sigma, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

ARMAModel

public ARMAModel(double mu,
                 double[] AR,
                 double[] MA,
                 double sigma)

Construct a univariate ARMA model.

Parameters:

mu - the intercept (constant) term

AR - the AR coefficients (excluding the initial 1); null if no AR coefficients

MA - the MA coefficients (excluding the initial 1); null if no MA coefficients

sigma - the white noise variance

ARMAModel

public ARMAModel(double mu,
                 double[] AR,
                 double[] MA)

Construct a univariate ARMA model with unit variance.

Parameters:

mu - the intercept (constant) term

AR - the AR coefficients (excluding the initial 1); null if no AR coefficient

MA - the MA coefficients (excluding the initial 1); null if no MA coefficient

ARMAModel

public ARMAModel(double[] AR,
                 double[] MA,
                 double sigma)

Construct a zero-intercept (mu) univariate ARMA model.

Parameters:

AR - the AR coefficients (excluding the initial 1); null if no AR coefficient

MA - the MA coefficients (excluding the initial 1); null if no MA coefficient

sigma - the white noise variance

ARMAModel

public ARMAModel(double[] AR,
                 double[] MA)

Construct a zero-intercept (mu) univariate ARMA model with unit variance.

Parameters:

AR - the AR coefficients (excluding the initial 1); null if no AR coefficient

MA - the MA coefficients (excluding the initial 1); null if no MA coefficient

ARMAModel

public ARMAModel(ARMAModel that)

Copy constructor.

Parameters:

that - a univariate ARMA model

Method Detail

armaMeanNoIntercept

public double armaMeanNoIntercept(double[] arLags,
                                  double[] maLags)

Compute the zero-intercept (mu) univariate ARMA conditional mean.

Parameters:

arLags - the AR lags

maLags - the MA lags

Returns:

the conditional mean

armaMean

public double armaMean(double[] arLags,
                       double[] maLags)

Compute the univariate ARMA conditional mean.

Parameters:

arLags - the AR lags

maLags - the MA lags

Returns:

the conditional mean