Package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma

Class AutoCorrelation

java.lang.Object

dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction

dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction

dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction

dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation

All Implemented Interfaces:

Function<Vector,Double>, BivariateRealFunction, 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 functions are ARMAacf and TacvfAR in package FitAR.

Nested Class Summary

Nested classes/interfaces inherited from interface dev.nm.analysis.function.Function

Function.EvaluationException

Constructor Summary

Constructors

Constructor	Description
AutoCorrelation(ARMAModel model, int nLags)	Compute the auto-correlation function for an ARMA model.

Method Summary

Modifier and Type	Method	Description
double	evaluate(double i)	Get the i-th auto-correlation.
double	evaluate(double i, double j)	Evaluate y = f(x1,x2).

Methods inherited from class dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction

get

Methods inherited from class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction

evaluate

Methods inherited from class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction

dimensionOfDomain, dimensionOfRange

Methods inherited from class java.lang.Object

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

Methods inherited from interface dev.nm.analysis.function.Function

dimensionOfDomain, dimensionOfRange

Constructor Details

AutoCorrelation

public AutoCorrelation(ARMAModel model,
 int nLags)

Compute the auto-correlation function for an ARMA model.

Parameters:

model - an ARIMA model

nLags - the number of lags

Method Details

evaluate

public double evaluate(double i,
 double j)

Description copied from interface: BivariateRealFunction

Evaluate y = f(x₁,x₂).

Parameters:

i - x₁

j - x₂

Returns:

f(x₁, x₂)

evaluate

public double evaluate(double i)

Get the i-th auto-correlation.

Parameters:

i - the lag order

Returns:

the i-th auto-correlation