com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample

Class PartialAutoCorrelation

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.sample.PartialAutoCorrelation

All Implemented Interfaces:

Function<Vector,java.lang.Double>, RealScalarFunction, java.io.Serializable

public class PartialAutoCorrelation
extends AutoCorrelationFunction

This computes the sample partial Auto-Correlation Function (PACF) for a univariate data set.

See Also:

"William W.S. Wei, "Section 2.5.4. Sample Partial Auto-correlation function" in Time Series Analysis : Univariate and Multivariate Methods (2nd Edition), Addison Wesley; 2 edition (July 17, 2005)", Serialized Form

Nested Class Summary

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

Function.EvaluationException

Constructor Summary

Constructors

Constructor and Description
PartialAutoCorrelation(TimeSeries xt)
PartialAutoCorrelation(TimeSeries xt, AutoCovariance.Type type)

Method Summary

Modifier and Type	Method and Description
double	evaluate(double x1, double x2) Evaluate y = f(x1,x2).
double	evaluate(int lag) Compute the partial auto-correlation for lag k.

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

get

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

dimensionOfDomain, dimensionOfRange, evaluate

Methods inherited from class java.lang.Object

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

Constructor Detail

PartialAutoCorrelation

public PartialAutoCorrelation(TimeSeries xt,
                              AutoCovariance.Type type)

PartialAutoCorrelation

public PartialAutoCorrelation(TimeSeries xt)

Method Detail

evaluate

public double evaluate(double x1,
                       double x2)

Description copied from class: BivariateRealFunction

Evaluate y = f(x₁,x₂).

Specified by:

evaluate in class BivariateRealFunction

Parameters:

x1 - x₁

x2 - x₂

Returns:

f(x₁, x₂)

evaluate

public double evaluate(int lag)

Compute the partial auto-correlation for lag k.

Parameters:

lag - lag >= 1

Returns:

ρ(k)