SuanShu, a Java numerical and statistical library

com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima
Class ArimaModel

java.lang.Object
  extended by com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel
      extended by com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaModel
Direct Known Subclasses:
ArmaModel

public class ArimaModel
extends ArimaxModel

This class represents a multivariate ARIMA model.


Field Summary
 
Fields inherited from class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel
mu, phi, psi, sigma, theta
 
Constructor Summary
ArimaModel(ArimaModel that)
          Copy constructor.
ArimaModel(ArimaModel model)
          Cast a univariate ARIMA model to a multivariate model.
ArimaModel(Matrix[] phi, int d, Matrix[] theta)
          Construct a zero-intercept (mu) multivariate ARIMA model with unit variance.
ArimaModel(Matrix[] phi, int d, Matrix[] theta, Matrix sigma)
          Construct a zero-intercept (mu) multivariate ARIMA model.
ArimaModel(Vector mu, Matrix[] phi, int d, Matrix[] theta)
          Construct a multivariate ARIMA model with unit variance.
ArimaModel(Vector mu, Matrix[] phi, int d, Matrix[] theta, Matrix sigma)
          Construct a multivariate ARIMA model.
 
Method Summary
 ArmaModel getArma()
          Get the ARMA specification of this ARIMA model, essentially ignoring the differencing.
 
Methods inherited from class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel
AR, AR, d, dimension, getArmax, MA, MA, maxPQ, mu, p, psi, q, sigma
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

ArimaModel

public ArimaModel(Vector mu,
                  Matrix[] phi,
                  int d,
                  Matrix[] theta,
                  Matrix sigma)
Construct a multivariate ARIMA model.

Parameters:
mu - the intercept (constant) vector
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
d - the order of integration
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient
sigma - the covariance matrix of white noise

ArimaModel

public ArimaModel(Vector mu,
                  Matrix[] phi,
                  int d,
                  Matrix[] theta)
Construct a multivariate ARIMA model with unit variance.

Parameters:
mu - the intercept (constant) vector
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
d - the order of integration
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient

ArimaModel

public ArimaModel(Matrix[] phi,
                  int d,
                  Matrix[] theta,
                  Matrix sigma)
Construct a zero-intercept (mu) multivariate ARIMA model.

Parameters:
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
d - the order of integration
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient
sigma - the covariance matrix of white noise

ArimaModel

public ArimaModel(Matrix[] phi,
                  int d,
                  Matrix[] theta)
Construct a zero-intercept (mu) multivariate ARIMA model with unit variance.

Parameters:
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
d - the order of integration
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient

ArimaModel

public ArimaModel(ArimaModel that)
Copy constructor.

Parameters:
that - a multivariate ARIMA model

ArimaModel

public ArimaModel(ArimaModel model)
Cast a univariate ARIMA model to a multivariate model.

Parameters:
model - a univariate ARIMA model
Method Detail

getArma

public ArmaModel getArma()
Get the ARMA specification of this ARIMA model, essentially ignoring the differencing.

Returns:
the ARMA specification

SuanShu, a Java numerical and statistical library

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