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

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

All Implemented Interfaces:

Function, RealScalarFunction

public class AutoCorrelation
extends AutoCorrelationFunction

Compute the Auto-Correlation Function (ACF) 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

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

Method Summary

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

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

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

AutoCorrelation

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

Compute the auto-correlation 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-correlation.

Parameters:

i - the lag

Returns:

the i-th auto-correlation