Package dev.nm.stat.timeseries.linear.univariate.sample

Class SamplePartialAutoCorrelation

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.sample.SamplePartialAutoCorrelation

All Implemented Interfaces:

Function<Vector,Double>, BivariateRealFunction, RealScalarFunction

public class SamplePartialAutoCorrelation
extends AutoCorrelationFunction

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

Nested Class Summary

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

Function.EvaluationException

Constructor Summary

Constructors

Constructor	Description
SamplePartialAutoCorrelation(IntTimeTimeSeries xt)	Construct the sample PACF for a time series.
SamplePartialAutoCorrelation(IntTimeTimeSeries xt, SampleAutoCovariance.Type type)	Construct the sample PACF for a time series.

Method Summary

Modifier and Type	Method	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 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

SamplePartialAutoCorrelation

public SamplePartialAutoCorrelation(IntTimeTimeSeries xt,
 SampleAutoCovariance.Type type)

Construct the sample PACF for a time series.

Parameters:

xt - a time series

type - the auto-covariance type

SamplePartialAutoCorrelation

public SamplePartialAutoCorrelation(IntTimeTimeSeries xt)

Construct the sample PACF for a time series.

Parameters:

xt - a time series

Method Details

evaluate

public double evaluate(double x1,
 double x2)

Description copied from interface: BivariateRealFunction

Evaluate y = f(x₁,x₂).

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 - the lag order; it must be > 1

Returns:

ρ(k)