com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma
Class AutoCovariance

java.lang.Object
  com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
      com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction
          com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.AutoCovariance

All Implemented Interfaces:

Function, RealScalarFunction

public class AutoCovariance
extends AutoCovarianceFunction

Compute the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.

This implementation solves the Yule-Walker equation.

The R equivalent function are ARMAacf and TacvfAR in package FitAR.

See Also:

"P. J. Brockwell and R. A. Davis, "p. 420. Eq. 11.3.15. The Covariance Matrix Function of a Causal ARMA Process. Chapter 11.3. Multivariate Time Series," in Time Series: Theory and Methods, Springer, 2006."

Nested Class Summary

Nested classes/interfaces inherited from interface com.numericalmethod.suanshu.analysis.function.Function

Function.EvaluationException

Constructor Summary

AutoCovariance(ArimaModel model, double wnVariance, int nLags)
Compute the auto-covariance function of an ARMA model.

Method Summary

double	evaluate(double i) Get the i-th auto-covariance.
double	evaluate(double x1, double x2) Compute f(x1, x2).

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

get

Methods inherited from class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction

dimension4Domain, dimension4Range, evaluate

Methods inherited from class java.lang.Object

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

Constructor Detail

AutoCovariance

public AutoCovariance(ArimaModel model,
                      double wnVariance,
                      int nLags)

Compute the auto-covariance function of an ARMA model.

Parameters:

model - an ARIMA specification

wnVariance - white noise variance

nLags - the number of lags in the result

Method Detail

evaluate

public double evaluate(double x1,
                       double x2)

Description copied from class: BivariateRealFunction

Compute f(x1, x2).

Specified by:

evaluate in class BivariateRealFunction

Parameters:

x1 - x1

x2 - x2

Returns:

f(x1, x2)

evaluate

public double evaluate(double i)

Get the i-th auto-covariance.

Parameters:

i - the lag

Returns:

the i-th auto-covariance