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

Class SampleAutoCovariance

java.lang.Object

dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction

dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction

dev.nm.stat.timeseries.linear.univariate.AutoCovarianceFunction

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

All Implemented Interfaces:

Function<Vector,Double>, BivariateRealFunction, RealScalarFunction

public class SampleAutoCovariance
extends AutoCovarianceFunction

This is the sample Auto-Covariance Function (ACVF) for a univariate data set.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static enum	SampleAutoCovariance.Type	the available auto-covariance types

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

Function.EvaluationException

Constructor Summary

Constructors

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

Method Summary

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

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

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

SampleAutoCovariance

public SampleAutoCovariance(IntTimeTimeSeries xt,
 SampleAutoCovariance.Type type)

Construct the sample ACVF for a time series.

Parameters:

xt - a time series

type - the auto-covariance type

SampleAutoCovariance

public SampleAutoCovariance(IntTimeTimeSeries xt)

Construct the sample ACVF for a time series.

Parameters:

xt - a time series

Method Details

evaluate

public double evaluate(int k)

Compute the auto-covariance for lag k.

Parameters:

k - the lag order

Returns:

γ(k)

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)